Title: Enhancing Object Detection Robustness: Detecting and Restoring Confidence in the Presence of Adversarial Patch Attacks Show Chinese title

Title: 增强目标检测的鲁棒性：在对抗补丁攻击存在时检测并恢复置信度

Abstract: The widespread adoption of computer vision systems has underscored their susceptibility to adversarial attacks, particularly adversarial patch attacks on object detectors. This study evaluates defense mechanisms for the YOLOv5 model against such attacks. Optimized adversarial patches were generated and placed in sensitive image regions, by applying EigenCAM and grid search to determine optimal placement. We tested several defenses, including Segment and Complete (SAC), Inpainting, and Latent Diffusion Models. Our pipeline comprises three main stages: patch application, object detection, and defense analysis. Results indicate that adversarial patches reduce average detection confidence by 22.06\%. Defenses restored confidence levels by 3.45\% (SAC), 5.05\% (Inpainting), and significantly improved them by 26.61\%, which even exceeds the original accuracy levels, when using the Latent Diffusion Model, highlighting its superior effectiveness in mitigating the effects of adversarial patches. Show Chinese abstract

Abstract: 计算机视觉系统的广泛应用凸显了它们对对抗性攻击的脆弱性，特别是对目标检测器的对抗性补丁攻击。 本研究评估了YOLOv5模型针对此类攻击的防御机制。 通过应用EigenCAM和网格搜索来确定最佳放置位置，生成并放置了优化的对抗性补丁到敏感图像区域。 我们测试了几种防御方法，包括分割和完整（SAC）、修复和潜在扩散模型。 我们的流程包括三个主要阶段：补丁应用、目标检测和防御分析。 结果表明，对抗性补丁将平均检测置信度降低了22.06%。 当使用潜在扩散模型时，防御措施分别恢复了3.45%（SAC）、5.05%（修复）的置信度，并显著提高了26.61%，甚至超过了原始准确率水平，突显了其在减轻对抗性补丁影响方面的优越有效性。