Understanding and Diagnosing Vulnerability under Adversarial Attacks
This work addresses the critical issue of adversarial vulnerability in deep learning models, offering insights for designing more robust architectures, though it is incremental as it builds on existing interpretability and robustness research.
The authors tackled the problem of understanding why deep neural networks are vulnerable to adversarial attacks by proposing InterpretGAN to interpret features and a diagnostic method to quantify layer-wise vulnerability, finding that layers with higher information loss are more vulnerable and average pooling is more robust than max pooling on MNIST and CIFAR10 datasets.
Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks. Currently, there is no clear insight into how slight perturbations cause such a large difference in classification results and how we can design a more robust model architecture. In this work, we propose a novel interpretability method, InterpretGAN, to generate explanations for features used for classification in latent variables. Interpreting the classification process of adversarial examples exposes how adversarial perturbations influence features layer by layer as well as which features are modified by perturbations. Moreover, we design the first diagnostic method to quantify the vulnerability contributed by each layer, which can be used to identify vulnerable parts of model architectures. The diagnostic results show that the layers introducing more information loss tend to be more vulnerable than other layers. Based on the findings, our evaluation results on MNIST and CIFAR10 datasets suggest that average pooling layers, with lower information loss, are more robust than max pooling layers for the network architectures studied in this paper.