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Tony Worrall One of the organisers of a rally being staged in Preston to show solidarity with the Palestinian people caug

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dable

Authors:
(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);
(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);
(3) Nicholas Carlini, Google DeepMind;
(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Table of Links Abstract and 1. Introduction Background and Related Work


Threat Model


Robust Style Mimicry


Experimental Setup


Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis


Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples B. Robust Mimicry Generations C. Detailed Results D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 F. Findings on Mist v2 G. Methods for Style Mimicry H. Existing Style Mimicry Protections I. Robust Mimicry Methods J. Experimental Setup K. User Study L. Compute Resources Abstract Artists are increasingly concerned about advancements in image generation models that can closely replicate their unique artistic styles. In response, several protection tools against style mimicry have been developed that incorporate small adversarial perturbations into artworks published online. In this work, we evaluate the effectiveness of popular protections—with millions of downloads—and show they only provide a false sense of security. We find that low-effort and “off-the-shelf” techniques, such as image upscaling, are sufficient to create robust mimicry methods that significantly degrade existing protections. Through a user study, we demonstrate that all existing protections can be easily bypassed, leaving artists vulnerable to style mimicry. We caution that tools based on adversarial perturbations cannot reliably protect artists from the misuse of generative AI, and urge the development of alternative protective solutions. 1 Introduction Style mimicry is a popular application of text-to-image generative models. Given a few images from an artist, a model can be finetuned to generate new images in that style (e.g., a spaceship in the style of Van Gogh). But style mimicry has the potential to cause significant harm if misused. In particular, many contemporary artists worry that others could now produce images that copy their unique art style, and potentially steal away customers (Heikkila¨, 2022). As a response, several protections have been developed to protect artists from style mimicry (Shan et al., 2023a; Van Le et al., 2023; Liang et al., 2023). These protections add adversarial perturbations to images that artists publish online, in order to inhibit the finetuning process. These protections have received significant attention from the media—with features in the New York Times (Hill, 2023), CNN (Thorbecke, 2023) and Scientific American (Leffer, 2023)—and have been downloaded over 1M times (Shan et al., 2023a). Yet, it is unclear to what extent these tools actually protect artists against style mimicry, especially if someone actively attempts to circumvent them (Radiya-Dixit et al., 2021). In this work, we show that state-of-the-art style protection tools—Glaze (Shan et al., 2023a), Mist (Liang et al., 2023) and Anti-DreamBooth (Van Le et al., 2023)—are ineffective when faced with simple robust mimicry methods. The robust mimicry methods we consider range from low-effort strategies—such as using a different finetuning script, or adding Gaussian noise to the images before training—to multi-step strategies that combine off-the-shelf tools. We validate our results with a user study, which reveals that robust mimicry methods can produce results indistinguishable in quality from those obtained from unprotected artworks (see Figure 1 for an illustrative example). We show that existing protection tools merely provide a false sense of security. Our robust mimicry methods do not require the development of new tools or fine-tuneing methods, but only carefully combining standard image processing techniques which already existed at the time that these protection tools were first introduced!. Therefore, we believe that even low-skilled forgers could have easily circumvented these tools since their inception. Although we evaluate specific protection tools that exist today, the limitations of style mimicry protections are inherent. Artists are necessarily at a disadvantage since they have to act first (i.e., once someone downloads protected art, the protection can no longer be changed). To be effective, protective tools face the challenging task of creating perturbations that transfer to any finetuning technique, even ones chosen adaptively in the future. A similar conclusion was drawn by Radiya-Dixit et al. (Radiya-Dixit et al., 2021), who argued that adversarial perturbations cannot protect users from facial recognition systems. We thus caution that adversarial machine learning techniques will not be able to reliably protect artists from generative style mimicry, and urge the development of alternative measures to protect artists. We disclosed our results to the affected protection tools prior to publication, so that they could determine the best course of action for existing users. This paper is available on arxiv under CC BY 4.0 license. Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Authors: Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work Threat Model Robust Style Mimicry Experimental Setup Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Background and Related Work Background and Related Work Background and Related Work Threat Model Threat Model Threat Model Robust Style Mimicry Robust Style Mimicry Robust Style Mimicry Experimental Setup Experimental Setup Experimental Setup Results 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis Results Results 6.1 Main Findings: All Protections are Easily Circumvented 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis 6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples A. Detailed Art Examples B. Robust Mimicry Generations B. Robust Mimicry Generations C. Detailed Results C. Detailed Results D. Differences with Glaze Finetuning D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 E. Findings on Glaze 2.0 F. Findings on Mist v2 F. Findings on Mist v2 G. Methods for Style Mimicry G. Methods for Style Mimicry H. Existing Style Mimicry Protections H. Existing Style Mimicry Protections I. Robust Mimicry Methods I. Robust Mimicry Methods J. Experimental Setup J. Experimental Setup K. User Study K. User Study L. Compute Resources L. Compute Resources Abstract Artists are increasingly concerned about advancements in image generation models that can closely replicate their unique artistic styles. In response, several protection tools against style mimicry have been developed that incorporate small adversarial perturbations into artworks published online. In this work, we evaluate the effectiveness of popular protections—with millions of downloads—and show they only provide a false sense of security. We find that low-effort and “off-the-shelf” techniques, such as image upscaling, are sufficient to create robust mimicry methods that significantly degrade existing protections. Through a user study, we demonstrate that all existing protections can be easily bypassed, leaving artists vulnerable to style mimicry. We caution that tools based on adversarial perturbations cannot reliably protect artists from the misuse of generative AI, and urge the development of alternative protective solutions. 1 Introduction Style mimicry is a popular application of text-to-image generative models. Given a few images from an artist, a model can be finetuned to generate new images in that style (e.g., a spaceship in the style of Van Gogh). But style mimicry has the potential to cause significant harm if misused. In particular, many contemporary artists worry that others could now produce images that copy their unique art style, and potentially steal away customers (Heikkila¨, 2022). As a response, several protections have been developed to protect artists from style mimicry (Shan et al., 2023a; Van Le et al., 2023; Liang et al., 2023). These protections add adversarial perturbations to images that artists publish online, in order to inhibit the finetuning process. These protections have received significant attention from the media—with features in the New York Times (Hill, 2023), CNN (Thorbecke, 2023) and Scientific American (Leffer, 2023)—and have been downloaded over 1M times (Shan et al., 2023a). Style mimicry Yet, it is unclear to what extent these tools actually protect artists against style mimicry, especially if someone actively attempts to circumvent them (Radiya-Dixit et al., 2021). In this work, we show that state-of-the-art style protection tools—Glaze (Shan et al., 2023a), Mist (Liang et al., 2023) and Anti-DreamBooth (Van Le et al., 2023)—are ineffective when faced with simple robust mimicry methods. The robust mimicry methods we consider range from low-effort strategies—such as using a different finetuning script, or adding Gaussian noise to the images before training—to multi-step strategies that combine off-the-shelf tools. We validate our results with a user study, which reveals that robust mimicry methods can produce results indistinguishable in quality from those obtained from unprotected artworks (see Figure 1 for an illustrative example). We show that existing protection tools merely provide a false sense of security. Our robust mimicry methods do not require the development of new tools or fine-tuneing methods, but only carefully combining standard image processing techniques which already existed at the time that these protection tools were first introduced!. Therefore, we believe that even low-skilled forgers could have easily circumvented these tools since their inception. Although we evaluate specific protection tools that exist today, the limitations of style mimicry protections are inherent. Artists are necessarily at a disadvantage since they have to act first (i.e., once someone downloads protected art, the protection can no longer be changed). To be effective, protective tools face the challenging task of creating perturbations that transfer to any finetuning technique, even ones chosen adaptively in the future. A similar conclusion was drawn by Radiya-Dixit et al. (Radiya-Dixit et al., 2021), who argued that adversarial perturbations cannot protect users from facial recognition systems. We thus caution that adversarial machine learning techniques will not be able to reliably protect artists from generative style mimicry, and urge the development of alternative measures to protect artists. We disclosed our results to the affected protection tools prior to publication, so that they could determine the best course of action for existing users. This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

New Research Reveals Vulnerabilities in Popular Art Protection Tools Against AI Theft

Новае даследаванне выяўляе ўразлівасці ў інструментах абароны папулярнага мастацтва ад крадзяжу AI

নতুন গবেষণা এআই চুরির বিরুদ্ধে জনপ্রিয় শিল্প সুরক্ষা সরঞ্জামগুলির দুর্বলতা প্রকাশ করে

Una nueva investigación revela vulnerabilidades en las herramientas de protección del arte popular contra el robo de IA

नए शोध से AI चोरी के खिलाफ लोकप्रिय कला संरक्षण उपकरणों में कमजोरियों का पता चला

Novo istraživanje otkriva ranjivosti popularnih alata za zaštitu umjetnina od AI krađe

Az új kutatás a mesterséges intelligencia-lopás elleni népszerű művészetvédelmi eszközök sebezhetőségeit tárja fel

新たな研究により、AIによる盗難に対する人気アート保護ツールの脆弱性が明らかに

ការស្រាវជ្រាវថ្មីបង្ហាញពីភាពងាយរងគ្រោះនៅក្នុងឧបករណ៍ការពារសិល្បៈដ៏ពេញនិយមប្រឆាំងនឹងការលួច AI

Nauji tyrimai atskleidžia populiarių meno apsaugos priemonių nuo AI vagysčių pažeidžiamumą

Ново истраживање открива рањивости популарних алата за заштиту уметности од крађе вештачке интелигенције

Uphando olutsha lutyhila ubuthathaka kwiZixhobo zoKhuseleko lwezobuGcisa ezidumileyo ngokuchasene nobusela be-AI

Ucwaningo Olusha Lembula Ubungozi Kumathuluzi Adumile Okuvikela Ubuciko Ngokumelene Nokwebiwa kwe-AI

Table of Links Abstract 1 Introduction 2 Mathematical Arguments 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 5 Existing Solutions 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.3 Literature Comparison 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.3 Discussion 9 Bifurcation Analysis 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 11 Policy Rule 12 Conclusions and References Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.3 De Rham Cohomology F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.3 Trend Inflation Volatility 5 Existing Solutions This section sets out the existing perturbation solutions, taken from ZINSS. There is a natural focus on the first order approximation, although I am able to generalize salient results to non-linear solutions. I set out familiar problems with their predictions concerning persistence and optimal policy. Next, I set out two alternative models of nominal rigidity the Lucas and Rotemberg Phillips curves. Up to parametric conditions, I connect these three models in a pseudo-observational equivalence, which I later disprove. This allows me to link this wrong claim back to a particular argument in Lucas Jr [1976]. Finally, I review all aspects of this famous paper. Author:
(1) David Staines. This paper is available on arxiv under CC 4.0 license. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Mathematical Arguments 2 Mathematical Arguments 3 Outline and Preview 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.2 Preferences 4.3 Household Equilibrium Conditions 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 4.8 Recursive Equilibrium 5 Existing Solutions 5 Existing Solutions 5.1 Singular Phillips Curve 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.3 Two Comparison Models 5.4 Lucas Critique 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.2 Equilibrium Construction 6.3 Literature Comparison 6.3 Literature Comparison 6.4 Equilibrium Analysis 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.1 Slope Coefficients 7.2 Error Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.2 Key Proofs 8.3 Discussion 8.3 Discussion 9 Bifurcation Analysis 9 Bifurcation Analysis 9.1 Analytic Aspects 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.2 Econometric Duality 10.3 Coefficient Properties 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 10.4 Microeconomic Interpretation 11 Policy Rule 11 Policy Rule 12 Conclusions and References 12 Conclusions and References Appendices Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.2 Behaviour of ∆ A.3 Proof Part (iii) A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.2 Basic Categories F.3 De Rham Cohomology F.3 De Rham Cohomology F.4 Marginal Costs and Inflation F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility 5 Existing Solutions This section sets out the existing perturbation solutions, taken from ZINSS. There is a natural focus on the first order approximation, although I am able to generalize salient results to non-linear solutions. I set out familiar problems with their predictions concerning persistence and optimal policy. Next, I set out two alternative models of nominal rigidity the Lucas and Rotemberg Phillips curves. Up to parametric conditions, I connect these three models in a pseudo-observational equivalence, which I later disprove. This allows me to link this wrong claim back to a particular argument in Lucas Jr [1976]. Finally, I review all aspects of this famous paper. Author: (1) David Staines. Author: Author: (1) David Staines. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

A Critical Review of Lucas Jr's Famous Paper on Nominal Rigidity

Once again Ross sat waiting for others to decide his future

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Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

“Never Invest In Something You Don’t Understand” This quote is one of the Gems by Warren Buffet and pretty much applies to all investments and across all the times. Without understanding what you are getting into don’t risk your hard earned money. Get to know the pros and cons of investing in cryptocurrencies. Only after your understanding the risks and rewards fully you should consider looking at dos and don’t of investing. Once you know the dos and don’ts then finally start investing. Do read this article completely before investing. I am sharing the relevant links at the bottom of this article, so that at-least you scroll down to the bottom of this article. Research, Research and Research If you are planning to invest in cryptocurrencies make sure to spend at-least 24 hours of effective reading time understanding what is blockchain, what are cryptocurrencies, security measures you should take before investing in cryptocurrencies and so on. Please remember that no research is enough and you need to keep updating yourself constantly. What could have been true a couple of months ago might be completely wrong now. Technology, Finance, Investment strategies, Regulation and every other thing about cryptos is changing at such a fast pace that you need to update yourself on a daily basis. In crypto lingo it is called DYOR : Do Your Own Research Get your hands Dirty A lot of people suggest to spend a lot of time understanding blockchain, cryptocurrencies and trading. I would suggest an alternate approach where you play around with a small amount so that you get your hands dirty and learn to play with real money and real exchanges. My suggestion is to play around with a small amount that you consider meagre. Create accounts, buy bitcoin, buy altcoins, do some trading, participate in a good ICO and so on. You can do all these with a small amount and once you are comfortable then you can bring in the big money. Know what FUD &FOMO means; Make sure not to be influenced by either of them These will be the two words that you will hear most frequently in the crypto markets. You resolve not to be influenced by either of these is what will give you peace of mind. FOMO : Fear of Missing Out. There were hordes of people who joined cryptocurrencies between November 2017 and and Jan 2018. Most of this crowd driven by FOMO. It was such a bull market that none of the new entrants had enough time to know the market before investing. It was one crazy bull run where some people doubled and tripled their money in less than a weeks time. Hearing such stories people didn’t want to miss out the opportunity. Today many of them will be wrecked as Bitcoin is at $6787 as of writing this article. Compared to the BTC all time high of $20089 this is 66% correction which most of them were not ready for. If they had done enough research either they would not have entered at such high levels or if they had decided to enter at such high levels they would have been prepared for the lows. But this doesn’t seem to be the case as many people are selling at losses and exiting the cryptocurrencies FUD : Fear, Uncertainty and Disinformation. This is one of the banes of social media and internet in general. Most of the time you will keep coming across fake news, manipulated images, tweets from fake accounts and likes. There will be interested parties who will spread Fear, Uncertainty and Disinformation to dump some coins. Learn to separate the fake news from the real. Trust but verify is the mantra to counter the FUD. If you hear something on these lines discuss it in the groups that you are part of so that you can verify the sources and intentions of any news item. Please be advised that even big institutions, new organisations, investment funds and governments can also be perpetuating FUD. It is not just Bitcoin there are a lot of others interesting cryptocurrencies. Bitcoin generally gets most of the press. The share of exposure that Bitcoin is so huge that Bitcoin is almost synonymous with Cryptocurrencies for the general public who are not exposed to cryptocurrencies. I invest only in coins that are backed by good projects. I generally keep referring to them as projects because that is what I am investing in. There are many good projects which can change the nature of the world we live. Things that were unimaginable just a couple of years ago could be reality thanks to the advancements in technology and the frictionless, trustless, automated communities/markets that are propping up all thanks to blockchains fuelled by cryptocurrencies. Join a couple of good Facebook Groups It can be very difficult to keep up to date with all the happenings in the Cryptosphere. There are way too many facebook and telegram groups. It is very difficult to identify the good ones unless you have subscribed to them. I would suggest against joining telegram groups initially as there will be a lot of chatter and can get irritating at times. I would suggest sticking to two facebook groups until you get a sense of things and you are ready for opening the gates for data flood. Bharata Finance Facebook Group : https://www.facebook.com/groups/BharataFinance/ CryptoCoinTrader Facebook Group : https://www.facebook.com/groups/218612071968287/ Do not join Pump and Dump Groups P&D groups as they are called and marketed can burn you very quickly. They generally promote their groups by promising 2x to 5x returns in day. You might get lucky with a few P&D and might make money as well. Though this can be very enticing don’t fall into the trap. I would suggest against joining this group for two reasons. They are trying to manipulate the markets and you are helping them in doing that. One, its wrong. P&D groups are market manipulators and scams. If you want to know how P&D groups work and how they can wreck you check out this article Two, you will get wrecked. https://bitfalls.com/2018/01/12/anatomy-pump-dump-group/ Learn to Track your Funds Unlike stock exchanges you will have to create accounts in multiple websites where you can buy Bitcoin and a multitude of other exchanges where you can trade your bitcoins or ethereum with a multitude of coins. As of today CoinmarketCap lists 1514 currencies on its site. It is difficult to keep track of these assets which will be distributed across different exchanges without a proper tool. Consider checkout a few portfolio trackers. is my favourite tool for tracking all my investments. It lets you auto-import your trades from multitude of exchanges and have some decent reports which lets you track your investments. You can read the complete manual I wrote on Cointracking https://steemit.com/cryptocurrency/@gokulnk/best-cryptocurrency-portfolio-manager Better safe than sorry I can’t over stress the importance of this. Make sure that take all the precautions necessary. Cryptocurrency is full of scams, hacks, phishing attacks, impostors and fraudsters. Always be careful before entering your passwords/private keys or transferring money to any accounts. Make sure to use a Linux/Mac OS, disable unnecessary extensions, always double check the urls(better to bookmark), never click on links from google search if you want to open an exchange website. The list goes on and on. I will create a separate post with the precautions that needs to be taken. Invest only what you can afford to lose You will keep hearing this as your start investing in Cryptos. This advice is like the “Terms and Conditions” that you find on the websites. With “Terms and Conditions” everybody signs it but nobody reads it. With respect to the advice “Invest only what you can afford to lose” everybody knows it but nobody follows it. There are multiple cases of people who lost all their savings, had taken loans for investing & lost most of it and are not able to pay the EMIs. I am nobody to decide on your risk appetite and your approach but remember that when you have not much to loose you can be much more confident about the projects that you have researched about. Be Proud I have lost the count and I am not sure if I have already listed down all the ten things. But I will still go ahead and mention this as I don’t see many people stating this. Irrespective of whether we loose or make money, every investor in cryptocurrency should be proud. If you have researched enough and if you have invested in worthy projects you should be proud as you are investing in the future of technology. I strongly believe that over the time If we can educate enough people about good projects we have an opportunity in-front of us which is unprecedented and can become a major catalyst in the history/future of Mankind. If we play our cards right I think we can reach Singularity (or at-least Utopia) before 2100. “Smart money moves towards Good Projects”. If you liked this article and would like to read similar articles, don’t forget to clap. Click and drag to clap more than once. 50 is the limit. If liked this article please check out other articles in the series. _It has been more than an year since I started investing in cryptocurrencies. Following Warren Buffet’s advice of “Never…_hackernoon.com Beginner’s Guide to “Investing in Cryptocurrencies” _Price is an important indicator. But it can also be misleading in many cases._blog.goodaudience.com Why comparing cryptocurrency prices is wrong _You can never be too paranoid in Cryptosphere. This might seem like a hyperbole but ask anybody who has lost their…_hackernoon.com Keep your Cryptocurrency Funds Safe _There are two camps in Cryptosphere. Bitcoin Maximalists and Altcoin champions. I first came across the below image…_hackernoon.com Bitcoin Dominance and the Emergence of “Others” _I first heard about Bitcoin back in 2011 and spent some time reading about it. I found it interesting but never…_hackernoon.com Track your cryptocurrency trades and portfolio

10 things to know/do before investing in cryptocurrencies

As a Web3 writer, I tend to shy away from deep tech topics. But when I heard Google’s latest announcement about their new quantum computing chip, Willow, I couldn’t look away. The headline-grabbing claim? A computational problem solved in 5 minutes that would take a classical supercomputer a staggering 10 septillion years to complete. For context, that’s 10,000,000,000,000,000,000,000,000 years — a number so vast that it makes the 13.8 billion-year age of the universe look like a lunch break. Naturally, the buzz was immediate: “Quantum computing is here to crack Bitcoin!” “No encryption is safe!” But hold your horses. Willow’s significance isn’t about rendering our current security systems obsolete overnight. It’s about a new way forward — for computing, for science, and yes, for crypto. Let’s break it down. What Exactly Is Google’s Willow? Google’s Willow chip is a quantum computing breakthrough. It’s not the largest quantum processor out there — it has 105 qubits. But what makes it revolutionary is how it deals with errors. Why Errors Matter in Quantum Computing? In traditional computers, bits represent either a 0 or a 1. But quantum computers use qubits, which can represent both 0 and 1 simultaneously thanks to the principles of superposition and entanglement. This ability lets quantum computers perform multiple calculations at once, potentially solving problems classical computers could never touch. The problem? Qubits are delicate. They’re easily disturbed by their environment, which leads to errors. And the more qubits you add, the more errors you typically get. This challenge has haunted quantum computing researchers for decades. Willow changes the game by demonstrating that as you add more qubits, the number of errors actually decreases. This is huge. It’s a critical step toward building large-scale, reliable quantum computers — the kind that could reshape industries like drug discovery, battery development, and material science. But where does that leave Bitcoin and crypto security? Should Crypto Holders Be Worried? The fear is simple: Quantum computers could theoretically break the cryptographic algorithms that secure Bitcoin and other cryptocurrencies. Bitcoin relies on two key pieces of encryption: 1 - SHA-256 for mining. 2 - ECDSA (Elliptic Curve Digital Signature Algorithm) for transaction signatures. Quantum computers could, in theory, brute-force these algorithms, rendering wallets and transactions vulnerable. If someone could crack ECDSA, they could steal funds by forging signatures. But here’s the thing: Willow isn’t that quantum computer. Not yet. According to tech experts, including Kevin Rose (former Google product manager), cracking Bitcoin’s encryption would require a quantum computer with about 13 million qubits — a far cry from Willow’s 105 qubits. So, no, you don’t need to sell your Bitcoin and go off-grid just yet. Why Quantum Computing Still Matters for Crypto Even though Willow can’t break Bitcoin’s encryption today, it’s a wake-up call. It shows that quantum computing isn’t some sci-fi fantasy. It’s evolving rapidly. And each breakthrough — like Willow’s error reduction — brings us closer to a world where quantum computers might pose a real threat to current cryptography. https://x.com/adamscochran/status/1866314726030143606 The Timeline Is Shortening Crypto venture capitalist Adam Cochran put it succinctly: Quantum computing advancements have shaved 20 years off the timeline for quantum threats to Bitcoin. Another breakthrough like Willow, and we might be looking at a 10-year window to prepare. This isn’t just about Bitcoin. Ethereum, smart contracts, and other blockchain-based systems rely on similar cryptographic protections. Quantum computing could jeopardize the entire Web3 ecosystem if we’re not ready. How Crypto Can Defend Itself Thankfully, the crypto community isn’t sitting idle. There are already discussions around post-quantum cryptography — encryption methods designed to withstand quantum attacks. 1 - Upgrading Bitcoin’s Security Crypto experts like Emin Gün Sirer, co-founder of Ava Labs, have suggested practical steps to protect Bitcoin: - A - Sunsetting Outdated Formats Early Bitcoins, mined by Satoshi Nakamoto, use an old format called P2PK (Pay-to-Public-Key). Unlike modern wallets, this format exposes public keys, making it vulnerable to future quantum decryption. Sirer recommends freezing these coins or setting a sunset date for P2PK transactions. B - Implementing Post-Quantum Algorithms Transitioning Bitcoin to quantum-resistant algorithms is complex but doable. It would likely require a hard fork and widespread adoption of new wallet software. 2 - Ethereum’s Preparedness Ethereum co-founder Vitalik Buterin has already proposed a strategy for Ethereum’s transition to post-quantum security. It would involve a hard fork and user cooperation to upgrade wallets, but it’s a clear and viable plan. Quantum Computing: A Threat or an Opportunity? While the threat to crypto security is real, quantum computing isn’t just about breaking things. It’s about building new possibilities. Imagine a world where quantum computers can: Simulate complex chemical reactions to discover life-saving drugs.


Optimize battery designs to extend electric vehicle range.


Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Quantum computing could lead to more secure blockchains, faster consensus algorithms, and decentralized systems that are truly unbreakable. The Real Takeaway Google’s Willow isn’t here to kill Bitcoin. It’s here to remind us that technology evolves, and we need to evolve with it. If you’re in Web3, now’s the time to: Stay Informed: Follow quantum computing advancements. They matter more than ever.
Advocate for Post-Quantum Security: Encourage discussions and research around quantum-resistant encryption.
Don’t Panic — Prepare: The future isn’t something to fear. It’s something to build. Quantum computing is coming. The question is: Will we be ready? What are your thoughts on the quantum threat to crypto? Are we prepared, or is it time to hit the panic button? As a Web3 writer, I tend to shy away from deep tech topics. But when I heard Google’s latest announcement about their new quantum computing chip, Willow, I couldn’t look away. The headline-grabbing claim? A computational problem solved in 5 minutes that would take a classical supercomputer a staggering 10 septillion years to complete. 10 septillion years For context, that’s 10,000,000,000,000,000,000,000,000 years — a number so vast that it makes the 13.8 billion-year age of the universe look like a lunch break. Naturally, the buzz was immediate: “Quantum computing is here to crack Bitcoin!” “No encryption is safe!” But hold your horses. Willow’s significance isn’t about rendering our current security systems obsolete overnight. It’s about a new way forward — for computing, for science, and yes, for crypto. Let’s break it down. What Exactly Is Google’s Willow? Google’s Willow chip is a quantum computing breakthrough. It’s not the largest quantum processor out there — it has 105 qubits. But what makes it revolutionary is how it deals with errors. Why Errors Matter in Quantum Computing? In traditional computers, bits represent either a 0 or a 1. But quantum computers use qubits, which can represent both 0 and 1 simultaneously thanks to the principles of superposition and entanglement. This ability lets quantum computers perform multiple calculations at once, potentially solving problems classical computers could never touch. The problem? Qubits are delicate. They’re easily disturbed by their environment, which leads to errors. And the more qubits you add, the more errors you typically get. This challenge has haunted quantum computing researchers for decades. Willow changes the game by demonstrating that as you add more qubits, the number of errors actually decreases. This is huge. It’s a critical step toward building large-scale, reliable quantum computers — the kind that could reshape industries like drug discovery, battery development, and material science. But where does that leave Bitcoin and crypto security? Should Crypto Holders Be Worried? The fear is simple: Quantum computers could theoretically break the cryptographic algorithms that secure Bitcoin and other cryptocurrencies. Bitcoin relies on two key pieces of encryption: 1 - SHA-256 for mining. 2 - ECDSA (Elliptic Curve Digital Signature Algorithm) for transaction signatures. Quantum computers could, in theory, brute-force these algorithms, rendering wallets and transactions vulnerable. If someone could crack ECDSA, they could steal funds by forging signatures. But here’s the thing: Willow isn’t that quantum computer. Not yet. According to tech experts, including Kevin Rose (former Google product manager), cracking Bitcoin’s encryption would require a quantum computer with about 13 million qubits — a far cry from Willow’s 105 qubits. According to tech experts, including Kevin Rose (former Google product manager), cracking Bitcoin’s encryption would require a quantum computer with about 13 million qubits — a far cry from Willow’s 105 qubits. So, no, you don’t need to sell your Bitcoin and go off-grid just yet. Why Quantum Computing Still Matters for Crypto Even though Willow can’t break Bitcoin’s encryption today, it’s a wake-up call. It shows that quantum computing isn’t some sci-fi fantasy. It’s evolving rapidly. And each breakthrough — like Willow’s error reduction — brings us closer to a world where quantum computers might pose a real threat to current cryptography. https://x.com/adamscochran/status/1866314726030143606 https://x.com/adamscochran/status/1866314726030143606 The Timeline Is Shortening Crypto venture capitalist Adam Cochran put it succinctly: Quantum computing advancements have shaved 20 years off the timeline for quantum threats to Bitcoin. Another breakthrough like Willow, and we might be looking at a 10-year window to prepare. The Timeline Is Shortening Crypto venture capitalist Adam Cochran put it succinctly: Quantum computing advancements have shaved 20 years off the timeline for quantum threats to Bitcoin. Another breakthrough like Willow, and we might be looking at a 10-year window to prepare. This isn’t just about Bitcoin. Ethereum, smart contracts, and other blockchain-based systems rely on similar cryptographic protections. Quantum computing could jeopardize the entire Web3 ecosystem if we’re not ready. How Crypto Can Defend Itself Thankfully, the crypto community isn’t sitting idle. There are already discussions around post-quantum cryptography — encryption methods designed to withstand quantum attacks. 1 - Upgrading Bitcoin’s Security Crypto experts like Emin Gün Sirer, co-founder of Ava Labs, have suggested practical steps to protect Bitcoin: - A - Sunsetting Outdated Formats A - Sunsetting Outdated Formats Early Bitcoins, mined by Satoshi Nakamoto, use an old format called P2PK (Pay-to-Public-Key). Unlike modern wallets, this format exposes public keys, making it vulnerable to future quantum decryption. Sirer recommends freezing these coins or setting a sunset date for P2PK transactions. B - Implementing Post-Quantum Algorithms B - Implementing Post-Quantum Algorithms Transitioning Bitcoin to quantum-resistant algorithms is complex but doable. It would likely require a hard fork and widespread adoption of new wallet software. 2 - Ethereum’s Preparedness Ethereum co-founder Vitalik Buterin has already proposed a strategy for Ethereum’s transition to post-quantum security. It would involve a hard fork and user cooperation to upgrade wallets, but it’s a clear and viable plan. Quantum Computing: A Threat or an Opportunity? While the threat to crypto security is real, quantum computing isn’t just about breaking things. It’s about building new possibilities. Imagine a world where quantum computers can: Simulate complex chemical reactions to discover life-saving drugs. Optimize battery designs to extend electric vehicle range. Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Simulate complex chemical reactions to discover life-saving drugs. Simulate complex chemical reactions to discover life-saving drugs. Optimize battery designs to extend electric vehicle range. Optimize battery designs to extend electric vehicle range. Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Quantum computing could lead to more secure blockchains, faster consensus algorithms, and decentralized systems that are truly unbreakable. The Real Takeaway Google’s Willow isn’t here to kill Bitcoin. It’s here to remind us that technology evolves, and we need to evolve with it. If you’re in Web3, now’s the time to: Stay Informed: Follow quantum computing advancements. They matter more than ever. Advocate for Post-Quantum Security: Encourage discussions and research around quantum-resistant encryption. Don’t Panic — Prepare: The future isn’t something to fear. It’s something to build. Quantum computing is coming. The question is: Will we be ready? Stay Informed: Follow quantum computing advancements. They matter more than ever. Advocate for Post-Quantum Security: Encourage discussions and research around quantum-resistant encryption. Don’t Panic — Prepare: The future isn’t something to fear. It’s something to build. Quantum computing is coming. The question is: Will we be ready? What are your thoughts on the quantum threat to crypto? Are we prepared, or is it time to hit the panic button?

Does Google’s Willow Quantum Chip Put Bitcoin’s Cryptography at Risk? 

গুগলের উইলো কোয়ান্টাম চিপ কি বিটকয়েনের ক্রিপ্টোগ্রাফিকে ঝুঁকিতে ফেলেছে? 

Sætter Googles Willow Quantum Chip Bitcoins kryptografi i fare? 

Το Willow Quantum Chip της Google θέτει σε κίνδυνο την κρυπτογραφία του Bitcoin; 

¿El chip cuántico Willow de Google pone en riesgo la criptografía de Bitcoin?

Apakah Chip Kuantum Willow Milik Google Membahayakan Kriptografi Bitcoin?

האם השבב הקוונטי של ווילו של גוגל מעמיד את הקריפטוגרפיה של ביטקוין בסיכון? 

Google の Willow Quantum チップはビットコインの暗号化を危険にさらすのか?

Vai Google Willow Quantum Chip apdraud Bitcoin kriptogrāfiju? 

Google's Willow Quantum Chip Bitcoin's Cryptography අවදානමට ලක් කරයිද? 

A e vë në rrezik çipi kuantik Willow i Google kriptografinë e Bitcoin? 

Оё Chip Quantum Willow Google криптографияи Bitcoin-ро зери хатар мегузорад? 

Google-ning Willow kvant chipi Bitcoin kriptografiyasini xavf ostiga qo'yadimi? 

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 2 Related Work Early text-to-image generative models, such as DALL-E (Ramesh et al., 2021) and CogView (Ding et al., 2021), or later Parti (Yu et al., 2022) employed autoregressive approaches but often suffered from significant content-level artifacts. This led to the development of a new breed of models that utilized the diffusion process to enhance image quality. Diffusion-based models, such as DALLE 2 (Ramesh et al., 2022), Imagen (Saharia et al., 2022b), and Stable Diffusion[6], have since become cornerstones in this domain. These models are typically divided into pixel-level (Ramesh et al., 2022; Saharia et al., 2022b) and latent-level (Rombach et al., 2022) approaches. This surge of interest has led to the design of innovative approaches and architectures, paving the way for numerous applications based on opensource generative models, such as DreamBooth (Ruiz et al., 2023) and DreamPose (Karras et al., 2023). These applications exploit image generation techniques to offer remarkable features, further fueling the popularity and the rapid development of diffusion-based image generation approaches. This enabled a wide array of applications like 3D object synthesis (Poole et al., 2023; Tang et al., 2023; Lin et al., 2022; Chen et al., 2023), video generation (Ho et al., 2022b; Luo et al., 2023; Ho et al., 2022a; Singer et al., 2023; Blattmann et al., 2023; Esser et al., 2023), controllable image editing (Hertz et al., 2023; Parmar et al., 2023; Liew et al., 2022; Mou et al., 2023; Lu et al., 2023), and more which are now at the forefront of this domain. Diffusion models achieve state-of-the-art results in image generation task both unconditional (Ho et al., 2020; Nichol and Dhariwal, 2021) and conditional (Peebles and Xie, 2022). They beat GANs (Goodfellow et al., 2014) by generating images with better scores of fidelity and diversity without adversarial training (Dhariwal and Nichol, 2021). Diffusion models also show the best performance in various image processing tasks like inpainting, outpainting, and super-resolution (Batzolis et al., 2021; Saharia et al., 2022a). Text-to-image diffusion models have become a popular research direction due to the high performance of diffusion models and the ability to simply integrate text conditions with the classifierfree guidance algorithm (Ho and Salimans, 2022). Early models like GLIDE (Nichol et al., 2022), Imagen (Saharia et al., 2022b), DALL-E 2 (Ramesh et al., 2022) and eDiff-I (Balaji et al., 2022) generate low-resolution image in pixel space and then upsample it with another super-resolution diffusion models. They are also using different text encoders, large language model T5 (Raffel et al., 2020) in Imagen, CLIP (Radford et al., 2021) in GLIDE and DALL-E 2. This paper is available on arxiv under CC BY 4.0 DEED license. [6] https://github.com/CompVis/stable-diffusion Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 2 Related Work Early text-to-image generative models, such as DALL-E (Ramesh et al., 2021) and CogView (Ding et al., 2021), or later Parti (Yu et al., 2022) employed autoregressive approaches but often suffered from significant content-level artifacts. This led to the development of a new breed of models that utilized the diffusion process to enhance image quality. Diffusion-based models, such as DALLE 2 (Ramesh et al., 2022), Imagen (Saharia et al., 2022b), and Stable Diffusion[6], have since become cornerstones in this domain. These models are typically divided into pixel-level (Ramesh et al., 2022; Saharia et al., 2022b) and latent-level (Rombach et al., 2022) approaches. This surge of interest has led to the design of innovative approaches and architectures, paving the way for numerous applications based on opensource generative models, such as DreamBooth (Ruiz et al., 2023) and DreamPose (Karras et al., 2023). These applications exploit image generation techniques to offer remarkable features, further fueling the popularity and the rapid development of diffusion-based image generation approaches. This enabled a wide array of applications like 3D object synthesis (Poole et al., 2023; Tang et al., 2023; Lin et al., 2022; Chen et al., 2023), video generation (Ho et al., 2022b; Luo et al., 2023; Ho et al., 2022a; Singer et al., 2023; Blattmann et al., 2023; Esser et al., 2023), controllable image editing (Hertz et al., 2023; Parmar et al., 2023; Liew et al., 2022; Mou et al., 2023; Lu et al., 2023), and more which are now at the forefront of this domain. Diffusion models achieve state-of-the-art results in image generation task both unconditional (Ho et al., 2020; Nichol and Dhariwal, 2021) and conditional (Peebles and Xie, 2022). They beat GANs (Goodfellow et al., 2014) by generating images with better scores of fidelity and diversity without adversarial training (Dhariwal and Nichol, 2021). Diffusion models also show the best performance in various image processing tasks like inpainting, outpainting, and super-resolution (Batzolis et al., 2021; Saharia et al., 2022a). Text-to-image diffusion models have become a popular research direction due to the high performance of diffusion models and the ability to simply integrate text conditions with the classifierfree guidance algorithm (Ho and Salimans, 2022). Early models like GLIDE (Nichol et al., 2022), Imagen (Saharia et al., 2022b), DALL-E 2 (Ramesh et al., 2022) and eDiff-I (Balaji et al., 2022) generate low-resolution image in pixel space and then upsample it with another super-resolution diffusion models. They are also using different text encoders, large language model T5 (Raffel et al., 2020) in Imagen, CLIP (Radford et al., 2021) in GLIDE and DALL-E 2. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [6] https://github.com/CompVis/stable-diffusion

Russian Scientists Identify Why Older Text-to-Image Models Couldn’t Create Realistic Images

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 4 Kandinsky Architecture In our work, we opted to deliver state-of-the-art text-to-image synthesis. In the initial stages of our research, we experimented with multilingual text encoders, such as mT5 (Xue et al., 2021), XLMR (Conneau et al., 2020), XLMR-CLIP[7], to facilitate robust multilingual text-to-image generation. However, we discovered that using the CLIP-image embeddings instead of standalone text encoders resulted in improved image quality. As a result, we adopted an image prior approach, utilizing diffusion and linear mappings between text and image embedding spaces of CLIP, while keeping additional conditioning with XLMR text embeddings. That is why Kandinsky uses two text encoders: CLIP-text with image prior mapping and XLMR. We have set these encoders to be frozen during the training phase. The significant factor that influenced our design choice was the efficiency of training latent diffusion models, as compared to pixel-level diffusion models (Rombach et al., 2022). This led us to focus our efforts on the latent diffusion architecture. Our model essentially comprises three stages: text encoding, embedding mapping (image prior), and latent diffusion. The construction of our model involves three primary steps: text encoding, embedding mapping (image prior), and latent diffusion. At the embedding mapping step, which we also refer to as the image prior, we use the transformer-encoder model. This model was trained from scratch with a diffusion process on text and image embeddings provided by the CLIP-ViT-L14 model. A noteworthy feature in our training process is the use of elementwise normalization of visual embeddings. This normalization is based on full-dataset statistics and leads to faster convergence of the diffusion process. We implemented inverse normalization to revert to the original CLIP-image embedding space in the inference stage. The image prior model is trained on text and image embeddings, provided by the CLIP models. We conducted a series of experiments and ablation studies on the specific architecture design of the image prior model (Table 3, Figure 6). The model with the best human evaluation score is based on a 1D-diffusion and standard transformer-encoder with the following parameters: num_layers=20, num_heads=32, and hidden_size=2048. The latent diffusion part employs a UNet model along with a custom pre-trained autoencoder. Our diffusion model uses a combination of multiple condition signals: CLIP-image embeddings, CLIPtext embeddings, and XLMR-CLIP text embeddings. CLIP-image and XLMR-CLIP embeddings are merged and utilized as an input to the latent diffusion process. Also, we conditioned the diffusion process on these embeddings by adding all of them to the time-embedding. Notably, we did not skip the quantization step of the autoencoder during diffusion inference as it leads to an increase in the diversity and the quality of generated images (cf. Figure 4). In total, our model comprises 3.3 B parameters (Table 2). We observed that the image decoding was our main bottleneck in terms of generated image quality; hence, we developed a Sber-MoVQGAN, our custom implementation of MoVQGAN (Zheng et al., 2022) with minor modifications. We trained this autoencoder on the LAION HighRes dataset (Schuhmann et al., 2022), obtaining the SotA results in image reconstruction. We released the weights and code for these models under an open source licence[11]. The comparison of our autoencoder with competitors can be found in Table 4. This paper is available on arxiv under CC BY 4.0 DEED license. [7] https://github.com/FreddeFrallan/ Multilingual-CLIP [8] https://github.com/Stability-AI/ stablediffusion [9] https://github.com/ai-forever/ru-dalle [10] https://github.com/gligen/GLIGEN [11] https://github.com/ai-forever/MoVQGAN Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 4 Kandinsky Architecture In our work, we opted to deliver state-of-the-art text-to-image synthesis. In the initial stages of our research, we experimented with multilingual text encoders, such as mT5 (Xue et al., 2021), XLMR (Conneau et al., 2020), XLMR-CLIP[7], to facilitate robust multilingual text-to-image generation. However, we discovered that using the CLIP-image embeddings instead of standalone text encoders resulted in improved image quality. As a result, we adopted an image prior approach, utilizing diffusion and linear mappings between text and image embedding spaces of CLIP, while keeping additional conditioning with XLMR text embeddings. That is why Kandinsky uses two text encoders: CLIP-text with image prior mapping and XLMR. We have set these encoders to be frozen during the training phase. The significant factor that influenced our design choice was the efficiency of training latent diffusion models, as compared to pixel-level diffusion models (Rombach et al., 2022). This led us to focus our efforts on the latent diffusion architecture. Our model essentially comprises three stages: text encoding, embedding mapping (image prior), and latent diffusion. The construction of our model involves three primary steps: text encoding, embedding mapping (image prior), and latent diffusion. At the embedding mapping step, which we also refer to as the image prior, we use the transformer-encoder model. This model was trained from scratch with a diffusion process on text and image embeddings provided by the CLIP-ViT-L14 model. A noteworthy feature in our training process is the use of elementwise normalization of visual embeddings. This normalization is based on full-dataset statistics and leads to faster convergence of the diffusion process. We implemented inverse normalization to revert to the original CLIP-image embedding space in the inference stage. The image prior model is trained on text and image embeddings, provided by the CLIP models. We conducted a series of experiments and ablation studies on the specific architecture design of the image prior model (Table 3, Figure 6). The model with the best human evaluation score is based on a 1D-diffusion and standard transformer-encoder with the following parameters: num_layers=20, num_heads=32, and hidden_size=2048. The latent diffusion part employs a UNet model along with a custom pre-trained autoencoder. Our diffusion model uses a combination of multiple condition signals: CLIP-image embeddings, CLIPtext embeddings, and XLMR-CLIP text embeddings. CLIP-image and XLMR-CLIP embeddings are merged and utilized as an input to the latent diffusion process. Also, we conditioned the diffusion process on these embeddings by adding all of them to the time-embedding. Notably, we did not skip the quantization step of the autoencoder during diffusion inference as it leads to an increase in the diversity and the quality of generated images (cf. Figure 4). In total, our model comprises 3.3 B parameters (Table 2). We observed that the image decoding was our main bottleneck in terms of generated image quality; hence, we developed a Sber-MoVQGAN, our custom implementation of MoVQGAN (Zheng et al., 2022) with minor modifications. We trained this autoencoder on the LAION HighRes dataset (Schuhmann et al., 2022), obtaining the SotA results in image reconstruction. We released the weights and code for these models under an open source licence[11]. The comparison of our autoencoder with competitors can be found in Table 4. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [7] https://github.com/FreddeFrallan/ Multilingual-CLIP [8] https://github.com/Stability-AI/ stablediffusion [9] https://github.com/ai-forever/ru-dalle [10] https://github.com/gligen/GLIGEN [11] https://github.com/ai-forever/MoVQGAN

Russian Scientists Say New AI Architecture Produces State-of-the-Art Text-to-Image Synthesis

Even though cryptocurrencies have given rise to new opportunities for both, scammers and hackers continue to operate with very different methods (and targets). It’s easy to imagine an ordinary person being scammed. Probably, you can say that your aunt, cousin, friend, or even yourself were affected. On the other hand, the idea of ​​an individual being “hacked” seems strange, and instead, it’s more logical to think of hackers targeting small and large businesses. Do you know why this happens? Well, not every predator out there eats the same prey. Sometimes, volume matters, some other times, ‘quality’ is what matters. They’re on the lookout for different victims because they have different skills and resources for hunting. Let’s learn more about this. Scammers vs. Hackers Scammers rely on social engineering, which means they exploit human psychology to manipulate their targets. Their victims are often everyday individuals who may not be particularly tech-savvy but are susceptible to persuasive language or emotional appeals. These criminals use charm and storytelling to build trust, promising easy money or exclusive opportunities, which entices their victims to lower their guard. This makes the average person, especially someone who isn’t well-versed in how financial systems or cryptocurrencies work, the perfect target for scams. They don’t even need to understand technology deeply, just know how to convince people to part with their money. This is why it’s so important to ask a lot of questions and research everything, every time. On the other hand, hackers generally pursue more lucrative or technically challenging targets, like corporations or financial institutions, because their skills are focused on breaching systems rather than persuading individuals. Hackers typically have strong technical expertise, knowing how to exploit vulnerabilities in software or networks to gain unauthorized access. This requires time, patience, and precision, as their goal is often large-scale theft or disruption. While some hackers may dabble in phishing, most of their attacks aim for significant financial gain, and thus, they target businesses that hold valuable data or assets. Both groups, however, share a common thread: they exploit weaknesses. Scammers focus on human weaknesses, like trust and lack of knowledge, while hackers concentrate on system vulnerabilities. However, in essence, social engineering and technical hacking are two sides of the same coin, both leveraging different kinds of gaps to achieve their malicious objectives. Common Attacks (Against Common Folks) Common crypto scams targeting everyday people usually play on the promise of easy wealth. One of the most prevalent scams involves fraudulent investment schemes. These scams lure victims with promises of high returns on small investments, often through fake trading platforms or “get rich quick” opportunities. Victims are encouraged to deposit their cryptocurrency with the scammer, who vanishes once the money is transferred. The appeal lies in the simplicity: invest a small amount and watch it grow exponentially—except the growth is a complete fabrication. Another frequent vector of attack is phishing, where scammers create fake websites or send emails that closely mimic legitimate crypto exchanges or wallets. Victims unknowingly enter their login details, allowing scammers to steal their funds. Phishing scams are especially dangerous because they rely on minor differences in URLs or website appearances, which are easy to overlook. Even cautious users can fall victim to these types of attacks, especially when they’re in a rush or under pressure. Fake giveaways and impersonation scams are also rampant. Scammers pose as well-known figures in the crypto world, often on social media, promising to double or triple any crypto sent to them as part of a “promotion” or “giveaway.” People, eager for free money, send their funds only to realize they’ve been tricked. These scams prey on greed and excitement, making them highly effective against a broad audience. There are other, more complex strategies, but that’s for another story. The Ideal Target for Scammers Crypto scammers often target individuals who have limited knowledge about cryptocurrency but are eager to profit from its rapid growth. These victims may not fully understand how Distributed Ledger Technology (DLT) or digital wallets work, making them more susceptible to fraudulent schemes. They capitalize on this lack of understanding by presenting offers that seem too good to pass up, such as guaranteed returns or low-risk investment opportunities. People unfamiliar with the volatility of crypto markets may fall for these promises, not realizing that legitimate investments rarely guarantee profits. Another ideal target for crypto scammers is someone who is easily influenced by social proof or endorsements. This group includes individuals who trust recommendations from influencers, celebrities, or even friends without thoroughly researching the platforms or offers being promoted. Scammers exploit this by impersonating well-known figures or creating fake testimonials to make their schemes appear credible. The fear of missing out (FOMO) drives many people to invest without conducting proper due diligence, which plays directly into the scammer’s hands. Additionally, people with a strong desire for quick financial gains, especially during economic uncertainty, are prime targets. These individuals may be more willing to take risks, making them vulnerable to scams like Ponzi schemes or fake ICOs (Initial Coin Offerings). They often ignore red flags in their pursuit of fast returns, assuming they can cash out before the scam collapses. Their impatience and desire for immediate profit make them easy prey for scammers who promise instant rewards with minimal effort. Become a Difficult Prey Despite the varying levels of knowledge, anyone can fall victim to a crypto scam, even those who consider themselves tech-savvy or experienced in cryptocurrency. Scammers often use sophisticated tactics, which can trick even the most cautious individuals. For example, an expert could unknowingly access a fake version of a legitimate crypto exchange or wallet due to a cleverly disguised phishing link, entering their private keys or credentials into a malicious website. No one is completely immune, as scams evolve constantly to exploit human error or momentary lapses in judgment. We can only aspire to be difficult prey. To become one, there are several key protection measures to follow: Use multi-factor authentication (MFA) for all accounts. In Obyte wallets, you can do this by creating a multidevice account from the Global Settings. Before using any crypto exchange (especially if it’s centralized), check if the team of the coin you’re trading has listed it or mentioned it on its official channels. For instance, Obyte mentions GBYTE Exchanges on its official website. Double-check URLs and email addresses for subtle irregularities. If you suspect that an email or website may not be legitimate, it’s likely a scam. Absolutely avoid sharing your private keys or their equivalents, such as seed words, with anyone. Private keys are called private because they must be private. Keep this irreplaceable data in a safe place. Research investment opportunities thoroughly, avoiding those with guaranteed returns. Never take something at face value; always do your own research. Read all you can about how everything in crypto works and how to recognize bad and good signs. This could save you some serious headaches later. Stay informed about the latest scam tactics to recognize them early. Featured Vector Image by Freepik Even though cryptocurrencies have given rise to new opportunities for both, scammers and hackers continue to operate with very different methods (and targets). It’s easy to imagine an ordinary person being scammed. Probably, you can say that your aunt, cousin, friend, or even yourself were affected. On the other hand, the idea of ​​an individual being “hacked” seems strange, and instead, it’s more logical to think of hackers targeting small and large businesses. Do you know why this happens? Well, not every predator out there eats the same prey. Sometimes, volume matters, some other times, ‘quality’ is what matters. They’re on the lookout for different victims because they have different skills and resources for hunting. Let’s learn more about this. Scammers vs. Hackers Scammers rely on social engineering, which means they exploit human psychology to manipulate their targets. Their victims are often everyday individuals who may not be particularly tech-savvy but are susceptible to persuasive language or emotional appeals. These criminals use charm and storytelling to build trust, promising easy money or exclusive opportunities, which entices their victims to lower their guard. Scammers rely on social engineering, which means they exploit human psychology to manipulate their targets. This makes the average person, especially someone who isn’t well-versed in how financial systems or cryptocurrencies work, the perfect target for scams. They don’t even need to understand technology deeply, just know how to convince people to part with their money. This is why it’s so important to ask a lot of questions and research everything, every time. On the other hand, hackers generally pursue more lucrative or technically challenging targets, like corporations or financial institutions, because their skills are focused on breaching systems rather than persuading individuals. Hackers typically have strong technical expertise, knowing how to exploit vulnerabilities in software or networks to gain unauthorized access. This requires time, patience, and precision, as their goal is often large-scale theft or disruption . While some hackers may dabble in phishing, most of their attacks aim for significant financial gain, and thus, they target businesses that hold valuable data or assets. This requires time, patience, and precision, as their goal is often large-scale theft or disruption Both groups, however, share a common thread: they exploit weaknesses. Scammers focus on human weaknesses, like trust and lack of knowledge, while hackers concentrate on system vulnerabilities. However, in essence, social engineering and technical hacking are two sides of the same coin, both leveraging different kinds of gaps to achieve their malicious objectives. Common Attacks (Against Common Folks) Common crypto scams targeting everyday people usually play on the promise of easy wealth. One of the most prevalent scams involves fraudulent investment schemes. These scams lure victims with promises of high returns on small investments, often through fake trading platforms or “get rich quick” opportunities. Victims are encouraged to deposit their cryptocurrency with the scammer, who vanishes once the money is transferred. The appeal lies in the simplicity: invest a small amount and watch it grow exponentially—except the growth is a complete fabrication. Common crypto scams Common crypto scams Another frequent vector of attack is phishing, where scammers create fake websites or send emails that closely mimic legitimate crypto exchanges or wallets. Victims unknowingly enter their login details, allowing scammers to steal their funds. Another frequent vector of attack is phishing, where scammers create fake websites or send emails that closely mimic legitimate crypto exchanges or wallets. Phishing scams are especially dangerous because they rely on minor differences in URLs or website appearances, which are easy to overlook. Even cautious users can fall victim to these types of attacks, especially when they’re in a rush or under pressure. Fake giveaways and impersonation scams are also rampant. Scammers pose as well-known figures in the crypto world, often on social media, promising to double or triple any crypto sent to them as part of a “promotion” or “giveaway.” People, eager for free money, send their funds only to realize they’ve been tricked. These scams prey on greed and excitement, making them highly effective against a broad audience. There are other, more complex strategies, but that’s for another story . another story another story The Ideal Target for Scammers Crypto scammers often target individuals who have limited knowledge about cryptocurrency but are eager to profit from its rapid growth. These victims may not fully understand how Distributed Ledger Technology (DLT) or digital wallets work, making them more susceptible to fraudulent schemes. They capitalize on this lack of understanding by presenting offers that seem too good to pass up, such as guaranteed returns or low-risk investment opportunities. People unfamiliar with the volatility of crypto markets may fall for these promises, not realizing that legitimate investments rarely guarantee profits. Distributed Ledger Technology Distributed Ledger Technology Another ideal target for crypto scammers is someone who is easily influenced by social proof or endorsements. This group includes individuals who trust recommendations from influencers, celebrities, or even friends without thoroughly researching the platforms or offers being promoted. Scammers exploit this by impersonating well-known figures or creating fake testimonials to make their schemes appear credible. The fear of missing out (FOMO) drives many people to invest without conducting proper due diligence, which plays directly into the scammer’s hands. Another ideal target for crypto scammers is someone who is easily influenced by social proof or endorsements. Additionally, people with a strong desire for quick financial gains, especially during economic uncertainty, are prime targets. These individuals may be more willing to take risks, making them vulnerable to scams like Ponzi schemes or fake ICOs (Initial Coin Offerings). They often ignore red flags in their pursuit of fast returns, assuming they can cash out before the scam collapses. Their impatience and desire for immediate profit make them easy prey for scammers who promise instant rewards with minimal effort. Become a Difficult Prey Despite the varying levels of knowledge, anyone can fall victim to a crypto scam, even those who consider themselves tech-savvy or experienced in cryptocurrency. Scammers often use sophisticated tactics, which can trick even the most cautious individuals. For example, an expert could unknowingly access a fake version of a legitimate crypto exchange or wallet due to a cleverly disguised phishing link, entering their private keys or credentials into a malicious website. No one is completely immune, as scams evolve constantly to exploit human error or momentary lapses in judgment. We can only aspire to be difficult prey. To become one, there are several key protection measures to follow: Use multi-factor authentication (MFA) for all accounts. In Obyte wallets, you can do this by creating a multidevice account from the Global Settings. Use multi-factor authentication (MFA) for all accounts. In Obyte wallets, you can do this by creating a multidevice account from the Global Settings. Use multi-factor authentication (MFA) for all accounts. In Obyte wallets , you can do this by creating a multidevice account from the Global Settings. Use multi-factor authentication (MFA) for all accounts. Obyte wallets Obyte wallets Before using any crypto exchange (especially if it’s centralized), check if the team of the coin you’re trading has listed it or mentioned it on its official channels. For instance, Obyte mentions GBYTE Exchanges on its official website. Before using any crypto exchange (especially if it’s centralized), check if the team of the coin you’re trading has listed it or mentioned it on its official channels . For instance, Obyte mentions GBYTE Exchanges on its official website. check if the team of the coin you’re trading has listed it or mentioned it on its official channels Obyte Obyte Double-check URLs and email addresses for subtle irregularities. If you suspect that an email or website may not be legitimate, it’s likely a scam. Double-check URLs and email addresses for subtle irregularities. If you suspect that an email or website may not be legitimate, it’s likely a scam. Double-check URLs and email addresses for subtle irregularities. Absolutely avoid sharing your private keys or their equivalents, such as seed words, with anyone. Private keys are called private because they must be private. Keep this irreplaceable data in a safe place. Absolutely avoid sharing your private keys or their equivalents, such as seed words, with anyone . Private keys are called private because they must be private. Keep this irreplaceable data in a safe place. Absolutely avoid sharing your private keys or their equivalents, such as seed words, with anyone Private keys Private keys Research investment opportunities thoroughly, avoiding those with guaranteed returns. Never take something at face value; always do your own research. Research investment opportunities thoroughly, avoiding those with guaranteed returns. Never take something at face value; always do your own research. Research investment opportunities thoroughly, avoiding those with guaranteed returns. Read all you can about how everything in crypto works and how to recognize bad and good signs. This could save you some serious headaches later. Read all you can about how everything in crypto works and how to recognize bad and good signs . This could save you some serious headaches later. Read all you can about how everything in crypto works and how to recognize bad and good signs how to recognize how to recognize Stay informed about the latest scam tactics to recognize them early. Stay informed about the latest scam tactics to recognize them early. Featured Vector Image by Freepik Featured Vector Image by Freepik Freepik Freepik

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