Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples -a visually indistinguishable adversarial image can easily be crafted to cause a well-trained model to misclassify. Existing methods for crafting adversarial examples are based on L2 and L∞ distortion metrics. However, despite the fact that L1 distortion accounts for the total variation and encourages sparsity in the perturbation, little has been developed for crafting L1based adversarial examples. In this paper, we formulate the process of attacking DNNs via adversarial examples as an elastic-net regularized optimization problem. Our elastic-net attacks to DNNs (EAD) feature L1-oriented adversarial examples and include the stateof-the-art L2 attack as a special case. Experimental results on MNIST, CIFAR10 and ImageNet show that EAD can yield a distinct set of adversarial examples and attains similar attack performance to the state-of-the-art methods in different attack scenarios. More importantly, EAD leads to improved attack transferability and complements adversarial training for DNNs, suggesting novel insights on leveraging L1 distortion in adversarial learning and security implications for DNNs.
Chen et al. extended C&W attack to L 1 distortion metrics and proposed an elastic-net regularized framework REF for adversarial generation.
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
ArXiv
ArXiv
Mathematics Computer Science