DePatch：面向真实世界中可规避人体检测器的鲁棒对抗补丁研究
DePatch: Towards Robust Adversarial Patch for Evading Person Detectors in the Real World
AI总结：
针对现有对抗补丁自耦合导致真实场景鲁棒性差的问题，提出DePatch解耦攻击方法，通过分块随机擦除、边界偏移和渐进解耦提升攻击能力，实验证明其性能优于同类物理对抗攻击。
摘要：
近年来，物理对抗攻击受到越来越多的关注，这类攻击旨在制作可部署的图案以欺骗深度神经网络，尤其是人体检测器。然而，现有基于补丁的攻击的对抗图案存在严重的自耦合问题：由物理变换导致的任意小补丁片段的退化，都可能造成对抗功能完全失效，使其在复杂真实世界中的鲁棒性较差。基于这一观察，我们提出了解耦对抗补丁（Decoupled adversarial Patch，DePatch）攻击，以解决对抗补丁的自耦合问题。具体而言，我们将对抗补丁划分为块状片段，并在优化过程中通过随机擦除部分片段来降低这些片段之间的相互依赖性。我们进一步引入了边界偏移操作和渐进式解耦策略，以提升整体攻击能力。大量实验表明，我们的方法性能优于其他物理对抗攻击，尤其是在真实世界场景中。
英文摘要：
Recent years have seen an increasing interest in physical adversarial attacks, which aim to craft deployable patterns for deceiving deep neural networks, especially for person detectors. However, the adversarial patterns of existing patch-based attacks heavily suffer from the self-coupling issue, where a degradation, caused by physical transformations, in any small patch segment can result in a complete adversarial dysfunction, leading to poor robustness in the complex real world. Upon this observation, we introduce the Decoupled adversarial Patch (DePatch) attack to address the self-coupling issue of adversarial patches. Specifically, we divide the adversarial patch into block-wise segments, and reduce the inter-dependency among these segments through randomly erasing out some segments during the optimization. We further introduce a border shifting operation and a progressive decoupling strategy to improve the overall attack capabilities. Extensive experiments demonstrate the superior performance of our method over other physical adversarial attacks, especially in the real world.
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