异常揭示：保护图像分类免受对抗性补丁攻击
Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks
AI总结：
针对对抗性补丁攻击，提出基于DBSCAN聚类的三段式防御，通过隔离并替换异常区域，将攻击下的分类准确率从38.8%提升至67.1%，优于现有方法。
摘要：
对抗性补丁攻击对深度学习系统的实际部署构成了重大威胁。然而，现有研究主要集中于图像预处理防御，这些方法往往导致干净图像的分类精度下降，且无法有效应对物理上可行的攻击。在本文中，我们研究了对抗性补丁在图像信息分布中作为异常的行为，并利用这一见解开发了一种稳健的防御策略。我们提出的防御机制利用一种基于聚类的技术DBSCAN来隔离异常图像片段，该过程通过一个包含分割、隔离和阻断三个阶段的三阶段流水线来识别和减轻对抗性噪声。在识别出对抗性组件后，我们通过用平均像素值替换它们来中和这些组件，这一方法优于其他替换选项。我们的模型无关防御机制在多个模型和数据集上进行了评估，证明了其在图像分类任务中有效对抗各种对抗性补丁攻击的能力。我们提出的方法显著提高了准确率，在针对LaVAN和GoogleAp攻击时，准确率从无防御时的38.8%提升至有防御时的67.1%，超过了诸如LGS（53.86%）和Jujutsu（60%）等著名的最先进方法。
英文摘要：
Adversarial patch attacks pose a significant threat to the practical deployment of deep learning systems. However, existing research primarily focuses on image pre-processing defenses, which often result in reduced classification accuracy for clean images and fail to effectively counter physically feasible attacks. In this paper, we investigate the behavior of adversarial patches as anomalies within the distribution of image information and leverage this insight to develop a robust defense strategy. Our proposed defense mechanism utilizes a clustering-based technique called DBSCAN to isolate anomalous image segments, which is carried out by a three-stage pipeline consisting of Segmenting, Isolating, and Blocking phases to identify and mitigate adversarial noise. Upon identifying adversarial components, we neutralize them by replacing them with the mean pixel value, surpassing alternative replacement options. Our model-agnostic defense mechanism is evaluated across multiple models and datasets, demonstrating its effectiveness in countering various adversarial patch attacks in image classification tasks. Our proposed approach significantly improves accuracy, increasing from 38.8\% without the defense to 67.1\% with the defense against LaVAN and GoogleAp attacks, surpassing prominent state-of-the-art methods such as LGS (53.86\%) and Jujutsu (60\%)
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
