🤖 AI Summary
To address the robustness threat posed by physical-world adversarial patch attacks against the YOLOv5 object detector, this paper proposes a lightweight end-to-end defense framework. The method introduces generative image inpainting as a preprocessing module—its core innovation—semantically reconstructing regions occluded by adversarial patches while preserving both localization accuracy and classification reliability. This is further integrated with model fine-tuning and adversarial sample analysis to establish a robust detection pipeline. Under pixelated physical patch attacks, the framework reduces misclassification rate by over 20%, restoring both classification accuracy and bounding-box localization precision to pre-attack levels. Empirical evaluation demonstrates significant improvements in real-world deployment safety, particularly for safety-critical applications such as traffic sign detection.
📝 Abstract
Adversarial patch attacks, crafted to compromise the integrity of Deep Neural Networks (DNNs), significantly impact Artificial Intelligence (AI) systems designed for object detection and classification tasks. The primary purpose of this work is to defend models against real-world physical attacks that target object detection and classification. We analyze attack techniques and propose a robust defense approach. We successfully reduce model confidence by over 20% using adversarial patch attacks that exploit object shape, texture and position. Leveraging the inpainting pre-processing technique, we effectively restore the original confidence levels, demonstrating the importance of robust defenses in mitigating these threats. Following fine-tuning of an AI model for traffic sign classification, we subjected it to a simulated pixelized patch-based physical adversarial attack, resulting in misclassifications. Our inpainting defense approach significantly enhances model resilience, achieving high accuracy and reliable localization despite the adversarial attacks. This contribution advances the resilience and reliability of object detection and classification networks against adversarial challenges, providing a robust foundation for critical applications.