Adversarial Camouflage

• URL: http://arxiv.org/abs/2603.21867v1
• Date: Mon, 23 Mar 2026 12:00:24 GMT
• Title: Adversarial Camouflage
• Abstract summary: We introduce textitAdrial Camouflage as a novel solution for protecting users' privacy.<n>The algorithm starts by defining a low-dimensional pattern space parameterized by color, shape, and angle.<n>It maximizes recognition error across multiple architectures, ensuring high cross-model transferability even against black-box systems.
• Score: 9.342851067813521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While the rapid development of facial recognition algorithms has enabled numerous beneficial applications, their widespread deployment has raised significant concerns about the risks of mass surveillance and threats to individual privacy. In this paper, we introduce \textit{Adversarial Camouflage} as a novel solution for protecting users' privacy. This approach is designed to be efficient and simple to reproduce for users in the physical world. The algorithm starts by defining a low-dimensional pattern space parameterized by color, shape, and angle. Optimized patterns, once found, are projected onto semantically valid facial regions for evaluation. Our method maximizes recognition error across multiple architectures, ensuring high cross-model transferability even against black-box systems. It significantly degrades the performance of all tested state-of-the-art face recognition models during simulations and demonstrates promising results in real-world human experiments, while revealing differences in model robustness and evidence of attack transferability across architectures.

Related papers

• Non-frontal face recognition using GANs and memristor-based classifiers [0.0]
  We propose a facial recognition framework that addresses non-frontal pose variations by integrating lightweight generative adversarial network (GAN)-based pose frontalisation with memristor-based neuromorphic recognition.<n>The experimental results on two datasets demonstrate the effectiveness of combining adversarial learning with memristive technology, achieving up to 96% identification accuracy.
  arXiv  Detail & Related papers  (2026-06-10T13:41:00Z)
• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• DiffMI: Breaking Face Recognition Privacy via Diffusion-Driven Training-Free Model Inversion [16.02881487974147]
  Face recognition poses serious privacy risks due to reliance on immutable biometric data.<n>Model inversion attacks reveal that identity information can still be recovered, exposing critical vulnerabilities.<n>We propose DiffMI, the first diffusion-driven, training-free model inversion attack.
  arXiv  Detail & Related papers  (2025-04-25T01:53:27Z)
• Local Features Meet Stochastic Anonymization: Revolutionizing Privacy-Preserving Face Recognition for Black-Box Models [54.88064975480573]
  The task of privacy-preserving face recognition (PPFR) currently faces two major unsolved challenges.<n>By disrupting global features while enhancing local features, we achieve effective recognition even in black-box environments.<n>Our method achieves an average recognition accuracy of 94.21% on black-box models, outperforming existing methods in both privacy protection and anti-reconstruction capabilities.
  arXiv  Detail & Related papers  (2024-12-11T10:49:15Z)
• Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain [16.05230409730324]
  Face image is a sensitive biometric attribute tied to the identity information of each user. This paper proposes a hybrid frequency-color fusion approach to reduce the input dimensionality of face recognition. It has around 2.6% to 4.2% higher accuracy than the state-of-the-art in the 1:N verification scenario.
  arXiv  Detail & Related papers  (2024-01-24T11:27:32Z)
• ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection [70.11264880907652]
  Recent object (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. We propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and camouflaged zooming in and out. Our framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks.
  arXiv  Detail & Related papers  (2023-10-31T06:11:23Z)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries [2.8532545355403123]
  Recent successful adversarial attacks on face recognition show that, despite the remarkable progress of face recognition models, they are still far behind the human intelligence for perception and recognition. In this paper, we propose automatic face warping which needs extremely limited number of queries to fool the target model. We evaluate the robustness of proposed method in the decision-based black-box attack setting.
  arXiv  Detail & Related papers  (2022-07-04T00:22:45Z)
• OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
  We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak. We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks. The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
  arXiv  Detail & Related papers  (2022-05-24T11:29:37Z)
• On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving [59.33715889581687]
  The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks. This paper presents an evaluation of the robustness of semantic segmentation models when attacked with different types of adversarial patches. A novel loss function is proposed to improve the capabilities of attackers in inducing a misclassification of pixels.
  arXiv  Detail & Related papers  (2022-01-05T22:33:43Z)
• Improving Transferability of Adversarial Patches on Face Recognition with Generative Models [43.51625789744288]
  We evaluate the robustness of face recognition models using adversarial patches based on transferability. We show that the gaps between the responses of substitute models and the target models dramatically decrease, exhibiting a better transferability.
  arXiv  Detail & Related papers  (2021-06-29T02:13:05Z)
• On the Robustness of Face Recognition Algorithms Against Attacks and Bias [78.68458616687634]
  Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged.
  arXiv  Detail & Related papers  (2020-02-07T18:21:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.