Deep Defense: Training DNNs with Improved Adversarial Robustness
Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN classifiers into making arbitrary predictions. To address this problem, we propose a training recipe named "deep defense". Our core idea is to integrate an adversarial perturbation-based regularizer into the classification objective, such that the obtained models learn to resist potential attacks, directly and precisely. The whole optimization problem is solved just like training a recursive network. Experimental results demonstrate that our method outperforms training with adversarial/Parseval regularizations by large margins on various datasets (including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code and models for reproducing our results are available at https://github.com/ZiangYan/deepdefense.pytorch.
The main idea of this paper is contribution of training recipe named "deep defense" that uses modification of classification loss with perturbation based regularizer and uses the encoder/decoder based architecture to achieve results on MNIST, CIFAR-10 and Image Net datasets that surpass ones from chosen reference implementation, DeepFool and FGS adversarial implementations and shows robustness that surpasses ones from above adversarial algorithms as well, by a wide margin.  The paper builds on the perturbation based regularization method for adversarial training with non-equal (weighted) treatment of robust examples (versus the others). The algorithm is described well, though sometimes than referring to the reference for the important details, would be good to include them, esp. in context of the setup, formulae and notations. The authors do a good job of supporting their reasoning for the choice of the perturbation regularization term, that has the advantage of utilizing the network architecture without any changes, and can be utilized in encoder/decoder framework, with initializations from pre-trained networks to achieve robustness to adversarial attacks while maintaining high accuracy on benign examples. The claims from the authors are well supported  by the results in Table 1 and 2 and the Figures 2-4 that show that Deep Defense beats the other two adversarial methods (Deepfool and FGS) methods on MNIST, CIFAR-10 datasets and is more robust across the board and also surpasses the reference network itself in benign accuracy including the ImageNet. They also demonstrate the impact of changing the hyperparameters c and lambda and layer regularization and how the accuracy varies with epochs. Authors intend to open source implementation.  A few areas of explanations and improvements that would help understanding the paper better are mentioned below  - It would be really interesting to add if the authors faced any challenges with deconvolution. In the literature both upsampling/convolution and deconvolution/unpooling have been used in decoders and both have pros/cons, though in the encoder/decoder setup (which the authors for some reason don't call as such), deconvolutions were employed by the authors and would be great to know if there were any challenges (esp. with deeper networks). Also, conspicuously the details on the decoder is missing from the description in the supplement. - details on R the exponential function in the perturbation regularization term aren't provided (its just mentioned as such) - the paper mentions all operations in CNN being differentiable and mention ReLU, that sounds off-point  - What is the stopping criteria (different epochs/batches/iterations are mentioned for different datasets but it wasn't clear  - In Table 1 the Perturbed examples from DeepFool were chosen when the Deep Defense drops to 50%, would good to know a) what if the perturbed data was instead chosen from one of the other two adversarial algorithms (so for e.g. when their performance drops to 50%) b) what happens below 50% was chosen to start with - For Image Net the authors conclude insufficient hyper-parameter exploration to include results for the two adversarial algorithms, would be good to include that in final version - The choice of the networks, why Alexnet and Resnet-18 were chosen (the authors make a remark about deep neworks, and would like to get their input on deeper architectures)