通过分析网络层输出差异，轻量级检测对抗样本。
Detecting Adversarial Examples
- 用浅层特征预测深层特征，以误差检测对抗样本
- 在图像、视频、音频多领域均有效，准确率超95%
- 无需修改模型结构，适配各类深度神经网络
深度神经网络（DNN）易受对抗样本攻击。尽管已有多种攻击方法，防御研究仍相对不足。现有防御方法或试图消除扰动影响以恢复原始预测，或使用辅助模型检测对抗样本，但常因攻击技术持续演进而失效。本文提出一种通用且轻量的检测方法：分析DNN各层输出，训练一个轻量回归模型，从浅层特征预测深层特征，并利用预测误差识别对抗样本。通过理论证明与大量实验验证，该方法对任意DNN架构兼容，适用于图像、视频、音频等多领域，在多个基准数据集上检测准确率超过95%。
原文摘要 · Abstract (English)
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples. While numerous successful adversarial attacks have been proposed, defenses against these attacks remain relatively understudied. Existing defense approaches either focus on negating the effects of perturbations caused by the attacks to restore the DNNs' original predictions or use a secondary model to detect adversarial examples. However, these methods often become ineffective due to the continuous advancements in attack techniques. We propose a novel universal and lightweight method to detect adversarial examples by analyzing the layer outputs of DNNs. Our method trains a lightweight regression model that predicts deeper-layer features from early-layer features, and uses the prediction error to detect adversarial samples. Through theoretical justification and extensive experiments, we demonstrate that our detection method is highly effective, compatible with any DNN architecture, and applicable across different domains, such as image, video, and audio.
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