Abstract
Adversarial attacks pose safety and security concerns for deep learning applications. Yet largely imperceptible, a strong PGD-like attack may leave strong trace in the adversarial example. Since attack triggers the local linearity of a network, we speculate network behaves in different extents of linearity for benign examples and adversarial examples. Thus, we construct Adversarial Response Characteristics (ARC) features to reflect the model's gradient consistency around the input to indicate the extent of linearity. Under certain conditions, it shows a gradually varying pattern from benign example to adversarial example, as the later leads to Sequel Attack Effect (SAE). ARC feature can be used for informed attack detection (perturbation magnitude is known) with binary classifier, or uninformed attack detection (perturbation magnitude is unknown) with ordinal regression. Due to the uniqueness of SAE to PGD-like attacks, ARC is also capable of inferring other attack details such as loss function, or the ground-truth label as a post-processing defense. Qualitative and quantitative evaluations manifest the effectiveness of ARC feature on CIFAR-10 w/ ResNet-18 and ImageNet w/ ResNet-152 and SwinT-B-IN1K with considerable generalization among PGD-like attacks despite domain shift. Our method is intuitive, light-weighted, non-intrusive, and data-undemanding.
Abstract (translated)
URL
https://arxiv.org/abs/2205.09586
