用因果推理检测对抗样本，无需额外训练。
CausAdv: A Causal-based Framework for Detecting Adversarial Examples
- 基于反事实推理学习输入的因果与非因果特征
- 对抗样本在最后一层卷积滤波器的反事实信息分布不同
- 可视化因果特征可辅助检测，适合安全敏感场景
深度学习在计算机视觉中取得巨大成功，主要得益于卷积神经网络（CNN）。然而，CNN易受精心设计的对抗扰动影响。这种对抗样本的脆弱性激发了通过对抗检测与防御方法提升模型鲁棒性的研究。本文通过因果推理解决CNN的对抗鲁棒性问题，提出CausAdv：一种基于反事实推理的对抗样本检测框架。CausAdv学习每个输入的因果与非因果特征，并量化最后一层卷积层每个滤波器的反事实信息（CI）。通过对干净样本与对抗样本的滤波器CI进行统计分析，发现对抗样本的CI分布显著不同于干净样本。结果表明，因果推理可有效提升检测能力，且无需训练独立检测器。同时，我们通过可视化提取的因果特征，展示了其作为检测工具的高效性。代码已公开于：https://github.com/HichemDebbi/CausAdv/tree/main。
原文摘要 · Abstract (English)
Deep learning has led to tremendous success in computer vision, largely due to Convolutional Neural Networks (CNNs). However, CNNs have been shown to be vulnerable to crafted adversarial perturbations. This vulnerability of adversarial examples has has motivated research into improving model robustness through adversarial detection and defense methods. In this paper, we address the adversarial robustness of CNNs through causal reasoning. We propose CausAdv: a causal framework for detecting adversarial examples based on counterfactual reasoning. CausAdv learns both causal and non-causal features of every input, and quantifies the counterfactual information (CI) of every filter of the last convolutional layer. We then perform a statistical analysis of the filters' CI across clean and adversarial samples, to demonstrate that adversarial examples exhibit different CI distributions compared to clean samples. Our results show that causal reasoning enhances the process of adversarial detection without the need to train a separate detector. Moreover, we illustrate the efficiency of causal explanations as a helpful detection tool by visualizing the extracted causal features. Code for reproducing our results is available at: https://github.com/HichemDebbi/CausAdv/tree/main.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。