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

2017-09-13 · Pin-Yu Chen, Yash Sharma, huan zhang, Jin-Feng Yi, Cho-Jui Hsieh

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. Our elastic-net attacks to DNNs (EAD) feature $L_1$-oriented adversarial examples and include the state-of-the-art $L_2$ attack as a special case. Experimental results on MNIST, CIFAR10 and ImageNet show that EAD can yield a distinct set of adversarial examples with small $L_1$ distortion 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 $L_1$ distortion in adversarial machine learning and security implications of DNNs.

📄 PDF Abstract BibTeX arXiv:1709.04114

Code (6)

ysharma1126/EAD-Attack 공식 구현 tf

BorealisAI/advertorch/blob/master/advertorch/attacks/ead.py pytorch

IBM/EAD-Attack tf

Trusted-AI/adversarial-robustness-toolbox/blob/main/art/attacks/evasion/elastic_net.py pytorch

bethgelab/foolbox/blob/master/foolbox/attacks/ead.py jax

cleverhans-lab/cleverhans/blob/master/cleverhans_v3.1.0/cleverhans/attacks/elastic_net_method.py tf

Tasks

Adversarial Attack Adversarial Robustness

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