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 6 Results In Figure 4, we report the distribution of success rates per artist (N=10) for each scenario. We averaged the quality and stylistic transfer success rates to simplify the analysis (detailed results can be found in Appendix C). Since the forger can try multiple mimicry methods for each prompt, and then decide which one worked best, we also evaluate a “best-of-4” method that picks the most successful mimicry method for each generation (according to human evaluators). 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). This paper is available on arxiv under CC BY 4.0 license. 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 6 Results In Figure 4, we report the distribution of success rates per artist (N=10) for each scenario. We averaged the quality and stylistic transfer success rates to simplify the analysis (detailed results can be found in Appendix C). Since the forger can try multiple mimicry methods for each prompt, and then decide which one worked best, we also evaluate a “best-of-4” method that picks the most successful mimicry method for each generation (according to human evaluators). 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). 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

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

How Easy Is It for AI To Mimic a Human Artist’s Style?

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

Art Protection Tools Fail Against Advanced AI Mimicry Methods

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

Why AI Style Protections Fall Short Against Advanced Mimicry Techniques

How AI Forgers Bypass Style Protections to Mimic Artists' Work

New Study Shows AI Can Now Mimic Art Styles More Accurately Than Ever

Art Protection Tools Fail Against Advanced AI Mimicry Methods

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

Why AI Style Protections Fall Short Against Advanced Mimicry Techniques

How AI Forgers Bypass Style Protections to Mimic Artists' Work

New Study Shows AI Can Now Mimic Art Styles More Accurately Than Ever

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps