EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Understand

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 $L_2$ and $L_\infty$ distortion metrics.
• However, despite the fact that $L_1$ distortion accounts for the total variation and encourages sparsity in the perturbation, little has been developed for crafting $L_1$-based adversarial examples.
• In this paper, we formulate the process of attacking DNNs via adversarial examples as an elastic-net regularized optimization problem.