A Single Set of Adversarial Clothes Breaks Multiple Defense Methods in the Physical World
- URL: http://arxiv.org/abs/2510.17322v1
- Date: Mon, 20 Oct 2025 09:16:25 GMT
- Title: A Single Set of Adversarial Clothes Breaks Multiple Defense Methods in the Physical World
- Authors: Wei Zhang, Zhanhao Hu, Xiao Li, Xiaopei Zhu, Xiaolin Hu,
- Abstract summary: Adversarial clothes provide a good test case for adversarial defense against patch-based attacks.<n>All the defense methods had poor performance against adversarial clothes in both the digital world and the physical world.<n>We crafted a single set of clothes that broke multiple defense methods on Faster R-CNN.
- Score: 27.46698744886066
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, adversarial attacks against deep learning-based object detectors in the physical world have attracted much attention. To defend against these attacks, researchers have proposed various defense methods against adversarial patches, a typical form of physically-realizable attack. However, our experiments showed that simply enlarging the patch size could make these defense methods fail. Motivated by this, we evaluated various defense methods against adversarial clothes which have large coverage over the human body. Adversarial clothes provide a good test case for adversarial defense against patch-based attacks because they not only have large sizes but also look more natural than a large patch on humans. Experiments show that all the defense methods had poor performance against adversarial clothes in both the digital world and the physical world. In addition, we crafted a single set of clothes that broke multiple defense methods on Faster R-CNN. The set achieved an Attack Success Rate (ASR) of 96.06% against the undefended detector and over 64.84% ASRs against nine defended models in the physical world, unveiling the common vulnerability of existing adversarial defense methods against adversarial clothes. Code is available at: https://github.com/weiz0823/adv-clothes-break-multiple-defenses.
Related papers
- The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections [74.60337113759313]
Current defenses against jailbreaks and prompt injections are typically evaluated against a static set of harmful attack strings.<n>We argue that this evaluation process is flawed. Instead, we should evaluate defenses against adaptive attackers who explicitly modify their attack strategy to counter a defense's design.
arXiv Detail & Related papers (2025-10-10T05:51:04Z) - PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection [27.75925749085402]
Adversarial Training has been recognized as the most effective defense against adversarial attacks.<n>We propose PBCAT, a novel Patch-Based Composite Adversarial Training strategy.
arXiv Detail & Related papers (2025-06-30T07:36:21Z) - Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense [90.7494670101357]
Ensemble everything everywhere is a defense to adversarial examples.<n>We show that this defense is not robust to adversarial attack.<n>We then use standard adaptive attack techniques to reduce the defense's robust accuracy.
arXiv Detail & Related papers (2024-11-22T10:17:32Z) - The Best Defense is a Good Offense: Adversarial Augmentation against
Adversarial Attacks [91.56314751983133]
$A5$ is a framework to craft a defensive perturbation to guarantee that any attack towards the input in hand will fail.
We show effective on-the-fly defensive augmentation with a robustifier network that ignores the ground truth label.
We also show how to apply $A5$ to create certifiably robust physical objects.
arXiv Detail & Related papers (2023-05-23T16:07:58Z) - Randomness in ML Defenses Helps Persistent Attackers and Hinders
Evaluators [49.52538232104449]
It is becoming increasingly imperative to design robust ML defenses.
Recent work has found that many defenses that initially resist state-of-the-art attacks can be broken by an adaptive adversary.
We take steps to simplify the design of defenses and argue that white-box defenses should eschew randomness when possible.
arXiv Detail & Related papers (2023-02-27T01:33:31Z) - 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) - Harnessing adversarial examples with a surprisingly simple defense [47.64219291655723]
I introduce a very simple method to defend against adversarial examples.
The basic idea is to raise the slope of the ReLU function at the test time.
Experiments over MNIST and CIFAR-10 datasets demonstrate the effectiveness of the proposed defense.
arXiv Detail & Related papers (2020-04-26T03:09:42Z) - 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.