Adversarial training, in which a network is trained on both adversarial and clean examples, is one of the most trusted defense methods against adversarial attacks. However, there are three major practical difficulties in implementing and deploying this method -expensive in terms of extra memory and computation costs; accuracy trade-off between clean and adversarial examples; and lack of diversity of adversarial perturbations. Classical adversarial training uses fixed, precomputed perturbations in adversarial examples (input space). In contrast, we introduce dynamic adversarial perturbations into the parameter space of the network, by adding perturbation biases to the fully connected layers of deep convolutional neural network. During training, using only clean images, the perturbation biases are updated in the Fast Gradient Sign Direction to automatically create and store adversarial perturbations by recycling the gradient information computed. The network learns and adjusts itself automatically to these learned adversarial perturbations. Thus, we can achieve adversarial training with negligible cost compared to requiring a training set of adversarial example images. In addition, if combined with classical adversarial training, our perturbation biases can alleviate accuracy trade-off difficulties, and diversify adversarial perturbations.
This paper proposes perturbation biases as a counter-measure against adversarial perturbations. The perturbation biases are additional bias terms that are trained by a variant of gradient ascent. The method imposes less computational costs compared to most adversarial training algorithms. In their experimental evaluation, the algorithm achieved higher accuracy on both clean and adversarial examples.
This paper should be rejected because the proposed method is not well justified either by theory or practice. Experiments are weak and do not support the effectiveness of the proposed method.
Major comments:
Since the evaluations of defense algorithms are often misleading [1], it requires throughout experiments or theoretical certifications to confirm the effectiveness of defense methods. However, the experiment configuration in this paper is not satisfactory to demonstrate the robustness of defended networks. The followings are a list of concerns.
1) Experiments are limited to small datasets and networks. Since some phenomena only appear in larger datasets [2], there is a concern that the proposed method also works on other datasets.
2) The attack algorithm used for the evaluation is weak. We can confirm this by observing the label leakage [2] in the experimental results. It is hard to judge which defenses are most effective, even within the tested datasets and models.
3) The "adversarial training" baseline used in the experiment is weird. Adversarial training typically generates adversarial examples during the process of the neural networks' optimization instead of using precomputed adversarial examples. Baseline methods should be stronger, for example, adversarial training with PGD [3].
[1] Athalye et al. "Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples." ICML 2018
[2] Kurakin et al. "ADVERSARIAL MACHINE LEARNING AT SCALE." ICLR 2017
[3] Madry et al. "Towards Deep Learning Models Resistant to Adversarial Attacks." ICLR 2018