标题： IPG：用于广义对抗性补丁训练的增量补丁生成 显示英文标题

标题： IPG: Incremental Patch Generation for Generalized Adversarial Patch Training

摘要： 对抗补丁的出现对AI模型的鲁棒性构成了重大挑战，特别是在计算机视觉任务如目标检测领域。与传统的对抗样本不同，这些补丁针对图像的特定区域，导致AI模型出现故障。本文提出了增量补丁生成（IPG），一种比现有方法效率高11.1倍的生成对抗补丁的方法，同时保持相当的攻击效果。IPG的有效性通过实验和消融研究得到证明，包括YOLO的特征分布可视化和对抗训练结果，这些结果表明它能生成泛化良好的补丁，有效覆盖更广泛的模型漏洞。此外，IPG生成的数据集可以作为构建鲁棒模型的坚实知识基础，使AI安全生态系统中的结构化表示、高级推理和主动防御成为可能。本研究的发现表明，IPG不仅在对抗补丁防御方面具有巨大潜力，而且在自动驾驶、安全系统和医学影像等现实应用中也具有广泛应用前景，其中AI模型必须在动态和高风险环境中保持对对抗攻击的弹性。 显示英文摘要

摘要： The advent of adversarial patches poses a significant challenge to the robustness of AI models, particularly in the domain of computer vision tasks such as object detection. In contradistinction to traditional adversarial examples, these patches target specific regions of an image, resulting in the malfunction of AI models. This paper proposes Incremental Patch Generation (IPG), a method that generates adversarial patches up to 11.1 times more efficiently than existing approaches while maintaining comparable attack performance. The efficacy of IPG is demonstrated by experiments and ablation studies including YOLO's feature distribution visualization and adversarial training results, which show that it produces well-generalized patches that effectively cover a broader range of model vulnerabilities. Furthermore, IPG-generated datasets can serve as a robust knowledge foundation for constructing a robust model, enabling structured representation, advanced reasoning, and proactive defenses in AI security ecosystems. The findings of this study suggest that IPG has considerable potential for future utilization not only in adversarial patch defense but also in real-world applications such as autonomous vehicles, security systems, and medical imaging, where AI models must remain resilient to adversarial attacks in dynamic and high-stakes environments.