Panoptic

panoptic

A holy grail of computer vision is the complete understanding of visual scenes. Progress in perceptual tasks began with the task of identifying what’s in the image, for example, whether there’s a man or not. Next, if there’s a man, we want to know where it is in the image. can draw a bounding box around it or label every pixel, e.g., whether the pixel belongs to a man or not. These tasks are called image classification, object detection, and image segmentation. AI What else can we say about the image? Was the man talking to a friend or running with a ball? To answer this question, AI needs to understand the interactions and relationships between objects in an image. This task is called Scene Graph Generation (SGG). Let’s discover how relationships between objects can be described. Relationships connect two objects. These relationships can be actions (jumping over), spatial (is behind), verbs (wear), prepositions (with), comparative (taller than), or prepositional phrases (drive on). Combining the objects and relationships, we create a directed graph representation (figure below). Objects can be represented by bounding boxes, from object detection, or by masks from panoptic segmentation. But panoptic segmentation has advantages, such as it covers the full scene with background and has better accuracy compared to the coarse bounding boxes. So, gives us a deeper scene understanding. Panoptic Scene Graph Generation (PSG) Understanding scenes would enable important applications such as image search, question answering, and robotic interactions. Although It seems easy for us, there’s a big perception gap between robotic systems and humans. This technology will allow them to do more intellectual work. Now, let’s dive into how this AI looks under the hood. We expect a set of triplets as an output of our neural net. Every triplet consists of two objects and their relationship. Such an output raises a couple of challenges for our network architecture. For example, should we predict all triplets simultaneously or just one by one? Can we solve the task in two stages: first performing object detection, then pairwise relationship prediction between these predicted objects? Or is it better to use a one-stage end-to-end neural net? There are multiple ways to approach this task. Let’s look at a promising one using a Transformer. The transformer has established itself as a surprisingly versatile network. It fits us because by using an attention mechanism, it can understand the relationship between sequential elements that are far from each other. As shown in the figure above, the PSGTR model first extracts image features from a CNN backbone and then feeds the features along with trainable queries and position encoding into a transformer encoder-decoder. Here, we expect the queries to learn the representation of scene graph triplets so that for each triplet query, the object/relationship/object predictions can be extracted by three individual Feed Forward Networks (FFNs), and the segmentation task can be completed by two panoptic heads for both objects, respectively. To train, the model authors created a dataset consisting of object/relationship/object triplets. The model predicts all triplets at once and is trained end-to-end with a set loss function that performs bipartite matching between prediction and ground truth. This is one way an . You can check the paper and code linked below for better understanding. AI understands a scene Reference Yang, Jingkang and Ang, Yi Zhe and Guo, Zujin and Zhou, Kaiyang and Zhang, Wayne and Liu, Ziwei. Panoptic Scene Graph Generation. Code: https://github.com/Jingkang50/OpenPSG Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, Li Fei-Fei. Visual Genome.

How An AI Understands Scenes: Panoptic Scene Graph Generation.

Uni-OVSeg: A Step Towards Efficient and Bias-Resilient Vision Systems

Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction 2. Related works 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.2. Main results 4.3. Ablation study 5. Conclusion 6. Broader impacts and References A. Framework details B. Promptable segmentation C. Visualisation 4.2. Main results Open-vocabulary semantic segmentation. As demonstrated in Tab. 1, our study presents a comprehensive comparison of our Uni-OVSeg against previous works across a range of benchmarks. These include ADE20K (encompassing both 150 and 847 class variants), PASCAL Context (459 and 59 class variants), PASCAL VOC (with 20 and 21 class categories), and Cityscapes. Compared to weakly-supervised methods, Uni-OVSeg exhibits remarkable performance improvements across all evaluated datasets. Specifically, in the more challenging datasets of PASCAL Context-459, Uni-OVSeg not only surpasses its weakly-supervised counterparts but also outperforms the cutting-edge fully-supervised methods, e.g., FC-CLIP. This is indicative of Uni-OVSeg’s superior capability in categorizing a diverse array of semantic classes. Furthermore, in the PASCAL VOC benchmarks (20 and 21 classes), Uni-OVSeg demonstrates a substantial enhancement over state-of-the-art weakly-supervised methods, achieving improvements of 18.3% and 12.2% mIoU, respectively, which demonstrates our Uni-OVSeg captures fine-grained spatial structures. These results elevate the practical applicability of weakly-supervised open-vocabulary segmentation to new heights. Open-vocabulary panoptic segmentation. For openvocabulary panoptic segmentation, we zero-shot evaluate our model on the COCO, ADE20K, and Cityscapes datasets, shown in Tab. 2. Existing weakly-supervised methods only use text supervision, which causes a challenge to discriminate different instances with the same semantic class. To the best of our knowledge, we are the first to learn open-vocabulary panoptic segmentation with weak supervision. Compared to unsupervised methods, we obviously outperform U2Seg by 1.9% PQ, 1.5%SQ, and 4.4% RQ on COCO datasets. Unfortunately, our used image-mask pairs contain multiple granularity masks, such as object-wise and part-wise masks, which is different with the panoptic segmentation datasets. This discrepancy leads to a number of false positive results, limiting our performance. Promptable segmentation. To evaluate the segmentation efficacy of Uni-OVSeg in scenarios involving interactive point and box prompts, we conduct comparative analyses with the SAM-ViT/L model across a range of datasets from diverse domains. For visual prompts, we implement a uniform 20×20 point grid as the interactive point prompt and utilise the actual bounding boxes as box prompts. The segmentation performance is measured using the 1-pt IoU (Oracle) metric across all datasets. The results, as demonstrated in Fig. 3, indicate that Uni-OVSeg exceeds SAM in most datasets when employing point and box prompts. In particular, Uni-OVSeg significantly surpasses SAM in terms of IoU by 31.1%, 20.1%, and 43.2% on the TimberSeg, COCO instance, and PIDray datasets, respectively. Additionally, we provide a visualisation of the segmentation results of both SAM and Uni-OVSeg in Fig. 4. The visualisation reveals that SAM tends to miss larger masks and erroneously categorise areas with unclear boundaries. In contrast, UniOVSeg demonstrates exceptional precision in segmenting objects of varying scales and different stuff. More experimental results are visualisation can be found in the Appendix B and C. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Authors: Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related works 2. Related works 3. Method and 3.1. Problem definition 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.1. Implementation details 4.2. Main results 4.2. Main results 4.3. Ablation study 4.3. Ablation study 5. Conclusion 5. Conclusion 6. Broader impacts and References 6. Broader impacts and References A. Framework details A. Framework details B. Promptable segmentation B. Promptable segmentation C. Visualisation C. Visualisation 4.2. Main results Open-vocabulary semantic segmentation. As demonstrated in Tab. 1, our study presents a comprehensive comparison of our Uni-OVSeg against previous works across a range of benchmarks. These include ADE20K (encompassing both 150 and 847 class variants), PASCAL Context (459 and 59 class variants), PASCAL VOC (with 20 and 21 class categories), and Cityscapes. Compared to weakly-supervised methods, Uni-OVSeg exhibits remarkable performance improvements across all evaluated datasets. Specifically, in the more challenging datasets of PASCAL Context-459, Uni-OVSeg not only surpasses its weakly-supervised counterparts but also outperforms the cutting-edge fully-supervised methods, e.g., FC-CLIP. This is indicative of Uni-OVSeg’s superior capability in categorizing a diverse array of semantic classes. Furthermore, in the PASCAL VOC benchmarks (20 and 21 classes), Uni-OVSeg demonstrates a substantial enhancement over state-of-the-art weakly-supervised methods, achieving improvements of 18.3% and 12.2% mIoU, respectively, which demonstrates our Uni-OVSeg captures fine-grained spatial structures. These results elevate the practical applicability of weakly-supervised open-vocabulary segmentation to new heights. Open-vocabulary semantic segmentation. Open-vocabulary panoptic segmentation. For openvocabulary panoptic segmentation, we zero-shot evaluate our model on the COCO, ADE20K, and Cityscapes datasets, shown in Tab. 2. Existing weakly-supervised methods only use text supervision, which causes a challenge to discriminate different instances with the same semantic class. To the best of our knowledge, we are the first to learn open-vocabulary panoptic segmentation with weak supervision. Compared to unsupervised methods, we obviously outperform U2Seg by 1.9% PQ, 1.5%SQ, and 4.4% RQ on COCO datasets. Unfortunately, our used image-mask pairs contain multiple granularity masks, such as object-wise and part-wise masks, which is different with the panoptic segmentation datasets. This discrepancy leads to a number of false positive results, limiting our performance. Open-vocabulary panoptic segmentation. Promptable segmentation. To evaluate the segmentation efficacy of Uni-OVSeg in scenarios involving interactive point and box prompts, we conduct comparative analyses with the SAM-ViT/L model across a range of datasets from diverse domains. For visual prompts, we implement a uniform 20×20 point grid as the interactive point prompt and utilise the actual bounding boxes as box prompts. The segmentation performance is measured using the 1-pt IoU (Oracle) metric across all datasets. The results, as demonstrated in Fig. 3, indicate that Uni-OVSeg exceeds SAM in most Promptable segmentation. datasets when employing point and box prompts. In particular, Uni-OVSeg significantly surpasses SAM in terms of IoU by 31.1%, 20.1%, and 43.2% on the TimberSeg, COCO instance, and PIDray datasets, respectively. Additionally, we provide a visualisation of the segmentation results of both SAM and Uni-OVSeg in Fig. 4. The visualisation reveals that SAM tends to miss larger masks and erroneously categorise areas with unclear boundaries. In contrast, UniOVSeg demonstrates exceptional precision in segmenting objects of varying scales and different stuff. More experimental results are visualisation can be found in the Appendix B and C. 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

Uni-OVSeg Outperforms Weakly-Supervised and Fully-Supervised Methods in Open-Vocabulary Segmentation

Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction 2. Related works 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.2. Main results 4.3. Ablation study 5. Conclusion 6. Broader impacts and References A. Framework details B. Promptable segmentation C. Visualisation 4.1. Implementation details Datasets. During training, we randomly sample the 30% subset from the SA-1B dataset [34], which contains ∼ 3 million images and ∼ 0.3 billion masks. Although this supervision provides diverse binary masks, it lacks the semantic class for each mask. In addition, following Chen et al. [8], we collect about 1.3 million image-text pairs and use a large vision-language model to refine them. Afterward, we use the ChatGPT-based parser to extract entities with descriptive words from these text descriptions. Evaluation & metrics. We evaluate our model mainly on three tasks, including open-vocabulary semantic segmentation, open-vocabulary panoptic segmentation, and promptable segmentation. Following previous work [76], we adopt prompt engineering from [21, 66] and prompt templates from [22, 37]. For open-vocabulary semantic segmentation, we zero-shot evaluate the model on the COCO [40], ADE20K [84], PASCAL [18] datasets. The open-vocabulary semantic segmentation results are evaluated with the mean Intersection-over-Union (mIoU). For open-vocabulary panoptic segmentation, we evaluate the model on the COCO, ADE20K, and Cityscapes [15] datasets. We report the panoptic quality (PQ), semantic quality (SQ), and recognition quality (RQ) for open-vocabulary panoptic segmentation. For promptable segmentation, we report the 1-Point and 1-Box IoU (Oracle) on a wide range of datasets. Oracle denotes that we select the output mask with the max IoU by calculating the IoU between the prediction and target mask. More details can be found in Appendix B. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Authors: Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related works 2. Related works 3. Method and 3.1. Problem definition 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.1. Implementation details 4.2. Main results 4.2. Main results 4.3. Ablation study 4.3. Ablation study 5. Conclusion 5. Conclusion 6. Broader impacts and References 6. Broader impacts and References A. Framework details A. Framework details B. Promptable segmentation B. Promptable segmentation C. Visualisation C. Visualisation 4.1. Implementation details Datasets . During training, we randomly sample the 30% subset from the SA-1B dataset [34], which contains ∼ 3 million images and ∼ 0.3 billion masks. Although this supervision provides diverse binary masks, it lacks the semantic class for each mask. In addition, following Chen et al. [8], we collect about 1.3 million image-text pairs and use a large vision-language model to refine them. Afterward, we use the ChatGPT-based parser to extract entities with descriptive words from these text descriptions. Datasets Evaluation & metrics. We evaluate our model mainly on three tasks, including open-vocabulary semantic segmentation, open-vocabulary panoptic segmentation, and promptable segmentation. Following previous work [76], we adopt prompt engineering from [21, 66] and prompt templates from [22, 37]. For open-vocabulary semantic segmentation, we zero-shot evaluate the model on the COCO [40], ADE20K [84], PASCAL [18] datasets. The open-vocabulary semantic segmentation results are evaluated with the mean Intersection-over-Union (mIoU). For open-vocabulary panoptic segmentation, we evaluate the model on the COCO, ADE20K, and Cityscapes [15] datasets. We report the panoptic quality (PQ), semantic quality (SQ), and recognition quality (RQ) for open-vocabulary panoptic segmentation. For promptable segmentation, we report the 1-Point and 1-Box IoU (Oracle) on a wide range of datasets. Oracle denotes that we select the output mask with the max IoU by calculating the IoU between the prediction and target mask. More details can be found in Appendix B. Evaluation & metrics. 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

Datasets and Evaluation Methods for Open-Vocabulary Segmentation Tasks

Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction 2. Related works 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.2. Main results 4.3. Ablation study 5. Conclusion 6. Broader impacts and References A. Framework details B. Promptable segmentation C. Visualisation 2. Related works Generic segmentation. Given an image, segmentation of specific visual concepts has remained an ongoing research topic in computer vision, as indicated by the extensive literature on it [23, 33, 44]. Generic segmentation mainly includes semantic segmentation [12, 44, 77, 79], instance segmentation [4, 7, 23], and panoptic segmentation [11, 11, 33, 56], related to different levels of granularity. In more detail, semantic segmentation [12, 20, 28, 38, 78] aims to assign a label to each pixel of the input image, according to their respective semantic classes. In addition, instance segmentation [53, 54, 59] attempts to distinguish different object instances of the same semantic class. Panoptic segmentation [9, 57, 74, 75] combines the characteristics of semantic segmentation and instance segmentation. Following a close-vocabulary assumption, previous works only can predict a predefined set of object categories. In this paper, we aim to build an advanced open-vocabulary segmentation system, which can categorise objects and stuff from an open set of vocabulary in the real world. Vision foundation models. Recent advancements in visual foundation models have diversified optimisation techniques across various learning paradigms. These developments range from vision-only pretraining [2, 24, 25, 61, 62] to joint vision-language pre-training [30, 48, 73], and extend to multi-modal frameworks that integrate visual prompting [1]. A prime example of this evolution is SAM [34], which shows the potential of extensive training for general segmentation, offering impressive generalisability and scalability. Despite its impressive capabilities, SAM cannot categorise predicted masks into different semantic classes, which is limited by the supervision of the image-mask pairs. More recently, Semantic-SAM [36] unifies different sources of human-annotated segmentation datasets and augments SAM by adding semantic labels and increased levels of granularity. In our work, our aim is to develop a more flexible vision foundation model, which can be trained with unpaired mask-text supervision (e.g., independent imagemask and image-text pairs) and can be easily adapted to different segmentation tasks. Open-vocabulary segmentation. Open-vocabulary segmentation counters the constraints of closed-vocabulary segmentation by allowing the segmentation of a diverse range of classes, even those unseen during training [21, 67, 68, 80]. Existing works [17, 66, 76] leverage the pretrained vision-language models (e.g., CLIP [48] and ALIGN [30]) to perform open-vocabulary segmentation. Most open-vocabulary segmentation methods commonly utilise human-annotated supervision (i.e., the image-masktext triplets) to generalise the capability of vision-language models from the image level to the pixel level. To reduce the dependency on this labour-intensive supervision, some weakly-supervised methods are proposed to use only text supervisions [46, 65]. They learn to group image regions into shaped segments, but struggle to distinguish different instances with the same semantic class and the segmentation performance is unsatisfactory [64, 85]. This dilemma drives our pursuit of more advanced open-vocabulary segmentation framework, where the aim is to enjoy as low annotation cost as possible and simultaneously achieve significant performance. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Authors: Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related works 2. Related works 3. Method and 3.1. Problem definition 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.1. Implementation details 4.2. Main results 4.2. Main results 4.3. Ablation study 4.3. Ablation study 5. Conclusion 5. Conclusion 6. Broader impacts and References 6. Broader impacts and References A. Framework details A. Framework details B. Promptable segmentation B. Promptable segmentation C. Visualisation C. Visualisation 2. Related works Generic segmentation. Given an image, segmentation of specific visual concepts has remained an ongoing research topic in computer vision, as indicated by the extensive literature on it [23, 33, 44]. Generic segmentation mainly includes semantic segmentation [12, 44, 77, 79], instance segmentation [4, 7, 23], and panoptic segmentation [11, 11, 33, 56], related to different levels of granularity. In more detail, semantic segmentation [12, 20, 28, 38, 78] aims to assign a label to each pixel of the input image, according to their respective semantic classes. In addition, instance segmentation [53, 54, 59] attempts to distinguish different object instances of the same semantic class. Panoptic segmentation [9, 57, 74, 75] combines the characteristics of semantic segmentation and instance segmentation. Following a close-vocabulary assumption, previous works only can predict a predefined set of object categories. In this paper, we aim to build an advanced open-vocabulary segmentation system, which can categorise objects and stuff from an open set of vocabulary in the real world. Generic segmentation. Vision foundation models. Recent advancements in visual foundation models have diversified optimisation techniques across various learning paradigms. These developments range from vision-only pretraining [2, 24, 25, 61, 62] to joint vision-language pre-training [30, 48, 73], and extend to multi-modal frameworks that integrate visual prompting [1]. A prime example of this evolution is SAM [34], which shows the potential of extensive training for general segmentation, offering impressive generalisability and scalability. Despite its impressive capabilities, SAM cannot categorise predicted masks into different semantic classes, which is limited by the supervision of the image-mask pairs. Vision foundation models. More recently, Semantic-SAM [36] unifies different sources of human-annotated segmentation datasets and augments SAM by adding semantic labels and increased levels of granularity. In our work, our aim is to develop a more flexible vision foundation model, which can be trained with unpaired mask-text supervision (e.g., independent imagemask and image-text pairs) and can be easily adapted to different segmentation tasks. Open-vocabulary segmentation. Open-vocabulary segmentation counters the constraints of closed-vocabulary segmentation by allowing the segmentation of a diverse range of classes, even those unseen during training [21, 67, 68, 80]. Existing works [17, 66, 76] leverage the pretrained vision-language models (e.g., CLIP [48] and ALIGN [30]) to perform open-vocabulary segmentation. Most open-vocabulary segmentation methods commonly utilise human-annotated supervision (i.e., the image-masktext triplets) to generalise the capability of vision-language models from the image level to the pixel level. To reduce the dependency on this labour-intensive supervision, some weakly-supervised methods are proposed to use only text supervisions [46, 65]. They learn to group image regions into shaped segments, but struggle to distinguish different instances with the same semantic class and the segmentation performance is unsatisfactory [64, 85]. This dilemma drives our pursuit of more advanced open-vocabulary segmentation framework, where the aim is to enjoy as low annotation cost as possible and simultaneously achieve significant performance. Open-vocabulary segmentation. 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

The Future of Segmentation: Low-Cost Annotation Meets High Performance

Understanding the Generalization Performance of GNNs: Topology Awareness and Future Directions

Centralization as a Factor Constraining the Development of Secure Communications

Reputation is among humankind's oldest currencies, yet in the digital age, its contours have shifted, expanded, and blurred in fascinating ways. It was in ancient times the force shaping tribal hierarchies, governing trade relations, and even determining survival. But in an increasingly decentralized, nonhierarchical world, reputation transcends simple notions of status and value. It has turned amorphous and multifaceted—a shadow we cast in an interwoven world shaped by perception, data, and context. Let's pause for a moment on this idea of reputation as a shadow. It's not something we control directly, though we might try to influence it. It grows or shrinks depending on the light cast on us—whether it's through public opinion, algorithms, or the ever-watchful eyes of social networks. But isn't it also a reflection of something more profound? Probably a reflection of how others perceive our actions, choices, and associations. But is reputation a reflection of who we really are, or is it just a story others tell themselves about us? The Fragmentation of Reputation in a Decentralized World Historically, reputation was often singular and local. You had a reputation within your village, within your guild, or within your court. It was a rather straightforward concept, invariably coupled with proximity and shared experiences. In contrast, today's decentralized, hyperconnected world fragments reputation into innumerable overlapping circles. Your reputation might be quite different on Twitter than on LinkedIn, than in your local community, or than on a niche forum dedicated to a passion you hold. In this world, reputation is no longer a static asset but a dynamic, ever-changing reflection of the multiplicity of identities we project. This multiplicity creates a curious tension: Can one ever be fully known, or is reputation always partial, incomplete? Each digital space we occupy provides only a sliver of the full story, leaving our reputations vulnerable to misinterpretation, amplification, or distortion. Consider the case of social media influencers. A well-recognized influencer in beauty, for instance, might have an excellent reputation in her community for being honest and creative; outside that circle, they might be dismissed as shallow or materialistic. Similarly, a person held in high regard for their technical expertise in one corner of the Internet could be unknown (or even disliked) in spheres where those skills are not appreciated. Reputation becomes, in a decentralized society, less about the notion of singular truth and more about navigating through a series of shifting truths. Algorithmic Reputation for Me Means The Digital Panopticon As we move into digital platforms, reputation now flows directly from algorithms and data points. Algorithms have become the new gatekeepers that curate and sort individuals based on perceived influence, trustworthiness, and engagement. It could be a credit score or a social network reputation system-invisible hands that mold and shape how others view and treat us, many times without our notice. This mediation through algorithms raises profound questions: Can reputation be trusted when it is distilled into data points?
If the proxy for reputation becomes followers, likes, and endorsers, are we running a serious risk of confusing the appearance of worthiness with integrity, knowledge, or skill? In many ways, the algorithm forms a kind of panopticon, a digital architecture in which we're always in a state of visibility, all performance and all ranking. But in ways quite different from Michel Foucault's panopticon, where a single centric authority could watch over us, today's panopticon is decentralized. We are being judged by everybody and nobody at the same time: peers, algorithms, and anonymous audiences. Reputation Without Hierarchy: Is It Possible? If we step away from reputation as a hierarchical one - where some are "above" and others "below"- we can begin to dream up new possibilities for what it might mean in a more equitable society. In that world, reputation may signal neither rank nor influence but rather signal one's dedication, contribution, or authenticity to a community. That is, it means thinking about reputation in light of shifting the static marker of worth into a dynamic signal of their alignment with the shared values and goals. For example, the open-source software community is sort of a prototype in that respect: the reputation here depends less on financial ability or other forms of power hierarchies. It's based on participation code, support, documentation, and idea development. One acquires reputation not by collecting followers or capital but by adding value to some common endeavor. Even here, in this ostensible flat landscape, reputation plays out through questions of access, influence, and trust. But can this scale outside of small, niche communities? Might we envision a world in which reputation is a function of values and commitments rather than of one's ability to game the system or perform for an audience? The idea of a decentralized, thus-free, manipulation-free reputation system most definitely is one that allured many, yet is highly fraught with risks. How do we prevent new forms of bias, exclusion, or exploitation from emerging? How do we make sure our reputation will stick to meaningful contributions and not superficial impressions? The Future of Reputation: Collective and Fluid We can only imagine that reputation will be even more collective and in flux in the future with the continued rise of decentralized technologies like blockchain. Consider a world in which reputation is decoupled from the performance of an individual but linked to an accumulation of collective experiences, networks, and communities. Your reputation may be a composite of the standing of groups you are part of, projects you work on, or causes you are identifying with. It is a vision that reorients reputation, moving it from the individual and onto the network: It's not about you, but about the company you keep, the ecosystems you nurture, and the values with which you align yourself. It makes reputation less of an achievement badge and more of an interconnection one. Recognizing that, in a decentralized world, none of us stand alone. In the future, reputation could be less something that's a shadow cast by our individual actions and more a constellation: a web of relationships and associations that define who we are within the context of a greater, dynamic system. In that process, this can yield more holistic ways of social capital, wherein the only way reputations are harnessed and maintained is through authentic, sustained participation in things greater than ourselves. Conclusion Once local and unified, reputation has splintered and dispersed itself through the digital world. No longer a badge of merit, it is an unfolding story, context-sensitive, written by algorithmic, communal, and networked agents of evaluation. So, on the way to a more decentralized society, this might also imply that reputation moves from being an individual signifier of standing into a communal, fluid articulation of our positions within larger ecologies. Reputation, at the bottom, is about value, but not necessarily value placed on us by others in some ranking system. It can instead be the value we add to the multiple, intersecting communities we live in and through, and the grace with which we surf the permutations of connection, contribution and trust. As we build this all anew, the challenge will be to make sure that this new kind of reputation is fair and meaningful - one that reflects who we truly are, as opposed to just what we appear to be. Reputation is among humankind's oldest currencies, yet in the digital age, its contours have shifted, expanded, and blurred in fascinating ways. It was in ancient times the force shaping tribal hierarchies, governing trade relations, and even determining survival. But in an increasingly decentralized, nonhierarchical world, reputation transcends simple notions of status and value. It has turned amorphous and multifaceted—a shadow we cast in an interwoven world shaped by perception, data, and context. Let's pause for a moment on this idea of reputation as a shadow. It's not something we control directly, though we might try to influence it. It grows or shrinks depending on the light cast on us—whether it's through public opinion, algorithms, or the ever-watchful eyes of social networks. But isn't it also a reflection of something more profound? Probably a reflection of how others perceive our actions, choices, and associations. But is reputation a reflection of who we really are, or is it just a story others tell themselves about us? The Fragmentation of Reputation in a Decentralized World Historically, reputation was often singular and local. You had a reputation within your village, within your guild, or within your court. It was a rather straightforward concept, invariably coupled with proximity and shared experiences. In contrast, today's decentralized, hyperconnected world fragments reputation into innumerable overlapping circles. Your reputation might be quite different on Twitter than on LinkedIn, than in your local community, or than on a niche forum dedicated to a passion you hold. In this world, reputation is no longer a static asset but a dynamic, ever-changing reflection of the multiplicity of identities we project. This multiplicity creates a curious tension: Can one ever be fully known, or is reputation always partial, incomplete? Can one ever be fully known, or is reputation always partial, incomplete? Each digital space we occupy provides only a sliver of the full story, leaving our reputations vulnerable to misinterpretation, amplification, or distortion. Consider the case of social media influencers. A well-recognized influencer in beauty, for instance, might have an excellent reputation in her community for being honest and creative; outside that circle, they might be dismissed as shallow or materialistic. Similarly, a person held in high regard for their technical expertise in one corner of the Internet could be unknown (or even disliked) in spheres where those skills are not appreciated. Reputation becomes, in a decentralized society, less about the notion of singular truth and more about navigating through a series of shifting truths. Algorithmic Reputation for Me Means The Digital Panopticon As we move into digital platforms, reputation now flows directly from algorithms and data points. Algorithms have become the new gatekeepers that curate and sort individuals based on perceived influence, trustworthiness, and engagement. It could be a credit score or a social network reputation system-invisible hands that mold and shape how others view and treat us, many times without our notice. This mediation through algorithms raises profound questions: Can reputation be trusted when it is distilled into data points? If the proxy for reputation becomes followers, likes, and endorsers, are we running a serious risk of confusing the appearance of worthiness with integrity, knowledge, or skill? Can reputation be trusted when it is distilled into data points? If the proxy for reputation becomes followers, likes, and endorsers, are we running a serious risk of confusing the appearance of worthiness with integrity, knowledge, or skill? In many ways, the algorithm forms a kind of panopticon, a digital architecture in which we're always in a state of visibility, all performance and all ranking. But in ways quite different from Michel Foucault's panopticon, where a single centric authority could watch over us, today's panopticon is decentralized. We are being judged by everybody and nobody at the same time: peers, algorithms, and anonymous audiences. Reputation Without Hierarchy: Is It Possible? If we step away from reputation as a hierarchical one - where some are "above" and others "below"- we can begin to dream up new possibilities for what it might mean in a more equitable society. In that world, reputation may signal neither rank nor influence but rather signal one's dedication, contribution, or authenticity to a community. That is, it means thinking about reputation in light of shifting the static marker of worth into a dynamic signal of their alignment with the shared values and goals. For example, the open-source software community is sort of a prototype in that respect: the reputation here depends less on financial ability or other forms of power hierarchies. It's based on participation code, support, documentation, and idea development. One acquires reputation not by collecting followers or capital but by adding value to some common endeavor. Even here, in this ostensible flat landscape, reputation plays out through questions of access, influence, and trust. But can this scale outside of small, niche communities? Might we envision a world in which reputation is a function of values and commitments rather than of one's ability to game the system or perform for an audience? The idea of a decentralized, thus-free, manipulation-free reputation system most definitely is one that allured many, yet is highly fraught with risks. How do we prevent new forms of bias, exclusion, or exploitation from emerging? How do we make sure our reputation will stick to meaningful contributions and not superficial impressions? The Future of Reputation: Collective and Fluid We can only imagine that reputation will be even more collective and in flux in the future with the continued rise of decentralized technologies like blockchain. Consider a world in which reputation is decoupled from the performance of an individual but linked to an accumulation of collective experiences, networks, and communities. Your reputation may be a composite of the standing of groups you are part of, projects you work on, or causes you are identifying with. It is a vision that reorients reputation, moving it from the individual and onto the network: It's not about you, but about the company you keep, the ecosystems you nurture, and the values with which you align yourself. It makes reputation less of an achievement badge and more of an interconnection one. Recognizing that, in a decentralized world, none of us stand alone. In the future, reputation could be less something that's a shadow cast by our individual actions and more a constellation: a web of relationships and associations that define who we are within the context of a greater, dynamic system. In that process, this can yield more holistic ways of social capital, wherein the only way reputations are harnessed and maintained is through authentic, sustained participation in things greater than ourselves. Conclusion Once local and unified, reputation has splintered and dispersed itself through the digital world. No longer a badge of merit, it is an unfolding story, context-sensitive, written by algorithmic, communal, and networked agents of evaluation. So, on the way to a more decentralized society, this might also imply that reputation moves from being an individual signifier of standing into a communal, fluid articulation of our positions within larger ecologies. Reputation, at the bottom, is about value, but not necessarily value placed on us by others in some ranking system. It can instead be the value we add to the multiple, intersecting communities we live in and through, and the grace with which we surf the permutations of connection, contribution and trust. As we build this all anew, the challenge will be to make sure that this new kind of reputation is fair and meaningful - one that reflects who we truly are, as opposed to just what we appear to be.

Reputation: The Shadow We Cast in a Networked World

السمعة: الظل الذي نلقيه في عالم الشبكات

Reputasiya: Şəbəkələşmiş Dünyada Tutduğumuz Kölgə

Reputació: L'ombra que projectem en un món en xarxa

Reputación: La sombra que proyectamos en un mundo interconectado

評判: ネットワーク化された世界で私たちが投げかける影

평판: 네트워크화된 세상에서 우리가 드리운 그림자

Botumo: Moriti wo re o Lahlelago Lefaseng leo le nago le Neteweke

شهرت: هغه سیوري چې موږ یې په شبکه شوې نړۍ کې اچولي

Reputação: A sombra que projetamos em um mundo em rede

Mukurumbira: Mumvuri Watinokanda Munyika Yakabatana

Sumcad: Hadhka Aan Ku Shubnay Dunida Isku Xidhka Ah

Danh tiếng: Cái bóng của chúng ta trong thế giới mạng

Inconsistencies Found in Craig Wright’s MYOB Bitcoin Transactions

Overview Digital fingerprinting is a technique used to identify users across different websites based on their unique device and browser characteristics. These characteristics - fingerprint parameters, can include various software, hardware (CPU, RAM, GPU, media devices - cameras, mics, speakers), location, time zone, IP, screen size/resolution, browser/OS languages, network, internet provider-related and other attributes. The combination of these parameters creates a unique identifier - fingerprint, that can be used to track a user's online activity. Fingerprints play a crucial role in online security, enabling services to identify and authenticate unique users. They also make it possible for users to trick such systems to stay anonymous online. However, if you can manipulate your fingerprints, you can run tens or hundreds or more different accounts to pretend that they are unique, authentic users. While this may sound cool, it has serious implications as it can make it possible to create an army of bots that can spread spam and fakes all over the internet, potentially resulting in fraudulent actions. Note: Obviously, I won’t discuss here how you can do "bad" things; you must always be careful, stay away from the “dark side,” and avoid committing illegal actions. This article is about the technology behind this, so use the information wisely. This is the first (and maybe not the last) article in this domain, so it will be more like an overview. The content offers an intro to the topic. There are dozens of tools and ways to detect, collect, and spoof fingerprints, and there are many different parameters and technologies that can reveal or hide your real identity or the fact that you're spoofing fingerprints (meaning you're not an authentic user). Parameters in Digital Fingerprints Let's consider some of them, some examples of obvious, simple, or well-known params, some that are rarer, less-know, and difficult to spoof. this string provides information about the user's browser, operating system, and device. User-Agent: it reveals the user's network and geo-location. Services use IP addresses for security and to prevent malicious activities. If you use one account from different IPs or many different accounts from the same IP, some services may view this activity (in combination with other params) as suspicious and use some level of bot protection against you. This can also be triggered if you use IPs that are already used or in some kind of banned list (proxies). IP: Information about installed plugins and extensions can be used to create a unique fingerprint - it helps in identifying users based on their browser's additional functionalities. Browser Plugins and Extensions: different users often have different display characteristics. Screen Resolution and Color Depth: they are important factors in fingerprinting - if you have random values, e.g., Japan timezone Norwegian language this is a bit unusual. Timezone and Language Settings: it involves rendering hidden graphics in the user's browser to gather information about the graphics hardware. Quite a tricky one because if you spoof your hardware info, then you can’t have the proper canvas value - you can’t render it on your different real hardware. Canvas: it exploits the unique capabilities and limitations of the user's graphics hardware in rendering 3D graphics - additional information about the user's device. WebGL Fingerprinting: the list of fonts installed on a user's system can be used as a fingerprinting parameter. This information is accessible through JavaScript. The list of fonts has to be realistic - so e.g. you can’t have Win fonts on macOS or you can’t have just 1 or 2 fonts, this is a very suspicious indicator. Fonts: it allows websites to determine the device's battery level and charging status, the combination of battery attributes can be used for fingerprinting. Battery status API: websites can use the web audio API to generate unique audio fingerprints by analyzing the audio processing characteristics of the device. Audio Fingerprinting: This information includes details about the software and hardware components of the device, such as the graphics card, network adapter, and operating system. This adds to the uniqueness of the fingerprint. Hardware Concurrency: This information includes details about the network connection, such as the IP address, ISP, and DNS server. Network Information: some users may use some software that listens to specific ports which also might be useful for some specific cases. Open and used ports: Navigating the Web incognito Digital fingerprint manipulation is a difficult task that demands a proactive approach to avoid detection. The are lots of strategies and tools to hide your identity (fingerprints) or pretend that you are someone else. Use privacy-focused browsers Opting for privacy-focused browsers, such as Brave, Ghostery, Tor, Octo Browser, or Vivaldi with enhanced privacy settings, provides a fundamental defense against common fingerprinting techniques. These browsers prioritize user privacy and incorporate features designed to hide your real fingerprints, making it harder to track your activities. For example, the Tor Browser, grounded in principles of anonymity, routes internet traffic through the Tor network. This strategic routing obscures the user's identity by bouncing connections through a series of volunteer-operated servers, enhancing overall online anonymity. Browser Extensions Privacy-centric browser extensions, including AdBlock, uBlock Origin, Privacy Badger, or CanvasBlocker, could be active defenses against tracking scripts, cookies, and fingerprinting attempts. These tools operate in the background, protecting and preserving user anonymity. VPN and Proxies The use of VPNs or proxy services adds an additional layer of protection by masking the user's real IPs (location, timezone, language, etc) and encrypting internet traffic. This is not only used to change your fingerprints but also to provide a more anonymous online presence and a higher security level. Motivations behind fingerprint spoofing Understanding the Whys Individuals concerned about personal privacy and the growing digital surveillance often spoof fingerprints to shield themselves from relentless online tracking. Privacy: Spoofing fingerprints prove useful for circumventing geo-restrictions, allowing users to access content restricted to specific regions. VPN and proxy services play a crucial role here by not only hiding the user's identity but also providing access to servers in different geographic locations. Geo-restriction: Avoiding targeted advertising and online profiling serves as a compelling motive for individuals to manipulate their digital fingerprints. Some tools can block 3rd-party tracking scripts and cookies, disrupting the profiling process. Ads: Web scraping, multi-accounting, e-commerce, bounty&airdrop, bonus-hunting, social networks bots, and affiliate marketing are often sources of income or bases for medium businesses. Digital agencies, individuals, and influencers engaged in these activities may require fingerprint spoofing to navigate through complicated bot detection systems. Avoiding detection becomes vital for some services, ensuring that legitimate activities are not mistakenly flagged or restricted (though quite often, such activities and accounts are not really legit but pretend to be so). Some of the most popular tools are Multilogin, X-Browser, Octo Browser, AdsPower, Incogniton, Scrapy, Surfsky, Web Scraper.io, ScrappingBee, etc. Strategic needs in the digital arena, bot detection, and protection systems: Collecting Users' Fingerprints Websites use JS (obviously) to harvest details about users to construct comprehensive digital portraits. This involves probing screen resolution, device orientation, mouse movements, keystroke dynamics, etc. Sophisticated fingerprinting scripts enumerate a wide range of browser and hardware attributes. JavaScript: Persistent cookies and data stored in local storage work to track users across sessions and platforms. Techniques include leveraging browser cookies and storing unique identifiers for user tracking. Depending on your purposes you need to know when you need an "empty" browser with unique fingerprints or when you need to trick a service showing that you have particular cookies and the same fingerprints. Cookies and local storage: The inclusion of 3rd-party scripts for analytics and advertising embeds invisible trackers often used to adjust systems behavior for a specific user based on collected fingerprints. You can use the aforementioned tools and approaches to selectively block or trick such scripts to get the needed behavior. 3rd-party scripts: Understanding Your Online Identity Panopticlick (EFF), Pixelscan, deviceinfo.me, and BrowserLeaks show your browser's fingerprint parameters such as User-Agent, canvas fingerprinting, fonts, etc. These checkers provide insights into the uniqueness and stability of your digital fingerprint across different browsing sessions. Online checkers: they allow users to inspect network requests, cookies, and other fingerprinting parameters, fostering a deeper understanding of their digital imprint and how websites detect bots and unique legit users and collect fingerprints. Examining the Network and Application tabs in browsers provides a real-time view of the data exchanged between the browser, websites, and servers. Browser developer tools: Here are some examples of how you can get some info about users' fingerprints in the browser. GEO: navigator.geolocation.getCurrentPosition(function(position) { var userLocation = position.coords; }); Microphone: navigator.mediaDevices.enumerateDevices() .then(function(devices) { var microphones = devices.filter(device => device.kind === 'audioinput'); }); Camera: navigator.mediaDevices.enumerateDevices() .then(function(devices) { var cameras = devices.filter(device => device.kind === 'videoinput'); }); Speakers: navigator.mediaDevices.enumerateDevices() .then(function(devices) { var speakers = devices.filter(device => device.kind === 'audiooutput'); }); Audio: var audioContext = new (window.AudioContext || window.webkitAudioContext)(); var oscillator = audioContext.createOscillator(); var analyser = audioContext.createAnalyser(); oscillator.connect(analyser); analyser.connect(audioContext.destination); GPU: var canvas = document.createElement('canvas'); var gl = canvas.getContext('webgl') || canvas.getContext('experimental-webgl'); var renderer = gl.getParameter(gl.RENDERER); console.log(renderer); Fonts: var fonts = []; var fontList = document.fonts.forEach(function(font) { fonts.push(font.family); }); console.log(fonts); Canvas (hashing): var canvas = document.createElement('canvas'); var context = canvas.getContext('2d'); var dataURL = canvas.toDataURL(); var canvasHash = MurmurHash3(dataURL); console.log(canvasHash); RAM and CPU: function getCPUInfo() { return navigator.hardwareConcurrency || 0; } function getDeviceMemory() { return navigator.deviceMemory || 0; } var cpuInfo = getCPUInfo(); var deviceMemory = getDeviceMemory(); console.log(`CPU Cores: ${cpuInfo}`); console.log(`Device Memory (GB): ${deviceMemory}`); Bot Detection Systems: Defending Against Automation A couple of examples of such systems: It is a global leader in bot detection and mitigation. It provides a proactive and precise way to verify that legitimate human users are accessing your website, mobile app, and APIs. Distil Networks uses a combination of techniques to detect bots. It studies a range of variables such as cursor movement, click patterns, and web browsing patterns across other websites. It uses , which checks all visitors against hundreds of different characteristics, focusing on their behavior. Distil Networks defends against web scraping, competitive data mining, account takeovers, transaction fraud, unauthorized vulnerability scans, spam, click fraud, denial of service, and API abuse. It can automatically block 99.9% of malicious traffic without impacting legitimate users. It also offers Distil Bot Defense for Web and API, which protect your website and API servers respectively. Distil Networks: Bot Detection: device fingerprints and Are You a Human technology It actively pulls additional data from the browser to identify devices with precision. When a browser request comes in, Distil interrogates the headers to see if the visitor is lying about their identity. This unique identifier can be used to fully or partially identify individual devices even when cookies cannot be read or stored in the browser, the client IP address is hidden, or one switches to another browser on the same device. It uses machine learning to detect biometric patterns in mouse activity and scrolling, as well as looking at browsers, devices, and other factors for clues. It can detect many simple or medium complexity bots that run JS from a web page. Resource Protection: It's a comprehensive cybersecurity platform that integrates fingerprint-based identification to discern legitimate users from potential threats. Utilizing advanced behavioral analysis and anomaly detection, Imperva builds profiles based on a combination of factors, including IP reputation, user-agent, different fingerprint params, and behavioral characteristics. This allows the system to detect suspicious activities indicative of bot traffic and mitigate potential threats effectively. Imperva: It's a prominent content delivery and cloud service provider that incorporates robust bot detection mechanisms within its security offerings. Leveraging a combination of fingerprint-based detection, behavior analysis, and machine learning, Akamai identifies and mitigates various bots. Akamai's global network allows for real-time threat intelligence, enabling proactive defense against evolving bot tactics. Akamai: It's a widely used content delivery network and security service that employs a multifaceted approach to bot detection. Cloudflare distinguishes between human users and bots by analyzing parameters such as IP reputation, user-agent characteristics, and behavioral patterns. The platform also utilizes threat intelligence and community-driven insights to stay ahead in the battle against emerging bot threats. Cloudflare uses to profile SSL/TLS clients and block potential bot requests. It also uses to apply rules and route traffic based on HTTP request information. is used to identify specific files and prevent data loss. These techniques help in detecting not real users. Cloudflare: JA3 Fingerprinting HTTP Filtering Data Fingerprinting Role of Bot Detection Systems: They play a pivotal role in enhancing bot detection capabilities. With servers strategically positioned around the globe, they can leverage geographical insights and real-time threat intelligence to identify and mitigate bot traffic effectively. The platform's WAF and bot managers contribute to a comprehensive defense against automated "users.” In this ongoing cat-and-mouse game, Bot Detection Systems continue to evolve, leveraging advanced technologies to stay ahead of fingerprint spoofing tech. The collaboration between security providers, businesses, and the wider online community remains crucial in defense against the tactics of automated threats but also, in a way, used against users' privacy, anonymity, and experience online, bringing intrusive ads and sophisticated user tracking tools that are only beneficial for businesses not for users. However, privacy protection, fingerprint hiding and spoofing, and ads and trackers blocking tools are also developing to evade even the most sophisticated user tracking systems. Websites' additional verification steps If a fingerprint appears suspicious, such as frequent changes or unusual combinations of data points, the website may trigger additional verification steps. Suspicious behavior: Websites may present users with captcha challenges to verify their identity. This involves solving puzzles or identifying distorted text, tasks that are difficult to perform for primitive bots. Captcha: Websites may request users to verify their mobile phone numbers to establish a stronger connection between a unique identity and a real-world entity. This can help prevent fraudulent activities. Mobile verification: the idea is the same as with mobile phones, but you need to submit your ID ID verification: Examples: Google doesn't require additional verification for new users with clear, consistent fingerprints. If a user's fingerprint changes frequently or resembles those used by known bots (or just already known for Google), Google may ask for a mobile phone number or ask to deal with a captcha. Financial websites often enforce stricter verification measures due to the sensitive nature of transactions. They may require users to provide additional personal information, verify their identity through secure channels, or pass more complex captcha challenges. Opinion: spoofing is better than hiding Hiding fingerprints can enhance privacy, security, and online anonymity, but spoofing them to appear as a unique, legitimate user offers even greater advantages. By blending in with the crowd and avoiding suspicion from systems that detect anti-detection tools, you can maintain credibility and trust. This approach allows you to enjoy the benefits of being perceived as a unique, legitimate user, minimizing the chance of facing additional protecting measures or obstacles. and, obviously, it helps in preventing leaking of your real data and identity. Also published . here

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