Fight Fire with Fire: Combating Adversarial Patch Attacks using Pattern-randomized Defensive Patches

• URL: http://arxiv.org/abs/2311.06122v2
• Date: Fri, 08 Nov 2024 03:55:37 GMT
• Title: Fight Fire with Fire: Combating Adversarial Patch Attacks using Pattern-randomized Defensive Patches
• Authors: Jianan Feng, Jiachun Li, Changqing Miao, Jianjun Huang, Wei You, Wenchang Shi, Bin Liang,
• Abstract summary: Object detection is susceptible to adversarial patch attacks. In this paper, we propose a novel and general methodology for defending adversarial attacks. Two types of defensive patches, canary and woodpecker, are specially-crafted and injected into the model input to proactively probe or counteract potential adversarial patches.
• Score: 12.329244399788669
• License:
• Abstract: Object detection has found extensive applications in various tasks, but it is also susceptible to adversarial patch attacks. The ideal defense should be effective, efficient, easy to deploy, and capable of withstanding adaptive attacks. In this paper, we adopt a counterattack strategy to propose a novel and general methodology for defending adversarial attacks. Two types of defensive patches, canary and woodpecker, are specially-crafted and injected into the model input to proactively probe or counteract potential adversarial patches. In this manner, adversarial patch attacks can be effectively detected by simply analyzing the model output, without the need to alter the target model. Moreover, we employ randomized canary and woodpecker injection patterns to defend against defense-aware attacks. The effectiveness and practicality of the proposed method are demonstrated through comprehensive experiments. The results illustrate that canary and woodpecker achieve high performance, even when confronted with unknown attack methods, while incurring limited time overhead. Furthermore, our method also exhibits sufficient robustness against defense-aware attacks, as evidenced by adaptive attack experiments.

Related papers

• Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks [62.036798488144306]
  Current defense mainly focuses on the known attacks, but the adversarial robustness to the unknown attacks is seriously overlooked. We propose an attack-agnostic defense method named Meta Invariance Defense (MID) We show that MID simultaneously achieves robustness to the imperceptible adversarial perturbations in high-level image classification and attack-suppression in low-level robust image regeneration.
  arXiv  Detail & Related papers  (2024-04-04T10:10:38Z)
• Improving behavior based authentication against adversarial attack using XAI [3.340314613771868]
  We propose an eXplainable AI (XAI) based defense strategy against adversarial attacks in such scenarios. A feature selector, trained with our method, can be used as a filter in front of the original authenticator. We demonstrate that our XAI based defense strategy is effective against adversarial attacks and outperforms other defense strategies.
  arXiv  Detail & Related papers  (2024-02-26T09:29:05Z)
• Confidence-driven Sampling for Backdoor Attacks [49.72680157684523]
  Backdoor attacks aim to surreptitiously insert malicious triggers into DNN models, granting unauthorized control during testing scenarios. Existing methods lack robustness against defense strategies and predominantly focus on enhancing trigger stealthiness while randomly selecting poisoned samples. We introduce a straightforward yet highly effective sampling methodology that leverages confidence scores. Specifically, it selects samples with lower confidence scores, significantly increasing the challenge for defenders in identifying and countering these attacks.
  arXiv  Detail & Related papers  (2023-10-08T18:57:36Z)
• A Random-patch based Defense Strategy Against Physical Attacks for Face Recognition Systems [3.6202815454709536]
  We propose a random-patch based defense strategy to robustly detect physical attacks for Face Recognition System (FRS) Our method can be easily applied to the real world face recognition system and extended to other defense methods to boost the detection performance.
  arXiv  Detail & Related papers  (2023-04-16T16:11:56Z)
• Guidance Through Surrogate: Towards a Generic Diagnostic Attack [101.36906370355435]
  We develop a guided mechanism to avoid local minima during attack optimization, leading to a novel attack dubbed Guided Projected Gradient Attack (G-PGA) Our modified attack does not require random restarts, large number of attack iterations or search for an optimal step-size. More than an effective attack, G-PGA can be used as a diagnostic tool to reveal elusive robustness due to gradient masking in adversarial defenses.
  arXiv  Detail & Related papers  (2022-12-30T18:45:23Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• BagFlip: A Certified Defense against Data Poisoning [15.44806926189642]
  BagFlip is a model-agnostic certified approach that can effectively defend against both trigger-less and backdoor attacks. We evaluate BagFlip on image classification and malware detection datasets.
  arXiv  Detail & Related papers  (2022-05-26T21:09:24Z)
• Defending Against Person Hiding Adversarial Patch Attack with a Universal White Frame [28.128458352103543]
  High-performance object detection networks are vulnerable to adversarial patch attacks. Person-hiding attacks are emerging as a serious problem in many safety-critical applications. We propose a novel defense strategy that mitigates a person-hiding attack by optimizing defense patterns.
  arXiv  Detail & Related papers  (2022-04-27T15:18:08Z)
• Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses [59.58128343334556]
  We introduce a relaxation term to the standard loss, that finds more suitable gradient-directions, increases attack efficacy and leads to more efficient adversarial training. We propose Guided Adversarial Margin Attack (GAMA), which utilizes function mapping of the clean image to guide the generation of adversaries. We also propose Guided Adversarial Training (GAT), which achieves state-of-the-art performance amongst single-step defenses.
  arXiv  Detail & Related papers  (2020-11-30T16:39:39Z)
• Certified Defenses for Adversarial Patches [72.65524549598126]
  Adversarial patch attacks are among the most practical threat models against real-world computer vision systems. This paper studies certified and empirical defenses against patch attacks.
  arXiv  Detail & Related papers  (2020-03-14T19:57:31Z)

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.