高阶对抗补丁可绕过实时目标检测，且泛化能力更强。
Higher-Order Adversarial Patches for Real-Time Object Detectors
- 迭代训练高阶对抗补丁与检测器，模拟攻防博弈。
- 高阶补丁使检测器误检率提升至37.6%，优于低阶攻击。
- 单纯对抗训练无法有效防御，适合安全测试场景。
高阶对抗攻击可视为一场持续的攻防博弈——攻击者不断追击、逼近，而防御者反复规避。本文研究高阶对抗攻击对目标检测器的影响，通过交替训练攻击补丁与检测器来实现。以YOLOv10为目标检测器，采用逃避攻击方式使用对抗补丁。结果表明，高阶对抗补丁不仅直接影响训练过的检测器，还展现出比低阶补丁更强的泛化能力。此外，实验显示仅靠对抗训练不足以高效抵御此类攻击。代码已开源：https://github.com/JensBayer/HigherOrder。
原文摘要 · Abstract (English)
Higher-order adversarial attacks can directly be considered the result of a cat-and-mouse game -- an elaborate action involving constant pursuit, near captures, and repeated escapes. This idiom describes the enduring circular training of adversarial attack patterns and adversarial training the best. The following work investigates the impact of higher-order adversarial attacks on object detectors by successively training attack patterns and hardening object detectors with adversarial training. The YOLOv10 object detector is chosen as a representative, and adversarial patches are used in an evasion attack manner. Our results indicate that higher-order adversarial patches are not only affecting the object detector directly trained on but rather provide a stronger generalization capacity compared to lower-order adversarial patches. Moreover, the results highlight that solely adversarial training is not sufficient to harden an object detector efficiently against this kind of adversarial attack. Code: https://github.com/JensBayer/HigherOrder
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