OTJR: Optimal Transport Meets Optimal Jacobian Regularization for Adversarial Robustness
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Deep neural networks are widely recognized as being vulnerable to adversarial perturbation. To overcome this challenge, developing a robust classifier is crucial. So far, two well-known defenses have been adopted to improve the learning of robust classifiers, namely adversarial training (AT) and Jacobian regularization. However, each approach behaves differently against adversarial perturbations. First, our work carefully analyzes and characterizes these two schools of approaches, both theoretically and empirically, to demonstrate how each approach impacts the robust learning of a classifier. Next, we propose our novel Optimal Transport with Jacobian regularization method, dubbed OTJR, jointly incorporating the input-output Jacobian regularization into the AT by leveraging the optimal transport theory. In particular, we employ the Sliced Wasserstein (SW) distance that can efficiently push the adversarial samples' representations closer to those of clean samples, regardless of the number of classes within the dataset. The SW distance provides the adversarial samples' movement directions, which are much more informative and powerful for the Jacobian regularization. Our extensive experiments demonstrate the effectiveness of our proposed method, which jointly incorporates Jacobian regularization into AT. Furthermore, we demonstrate that our proposed method consistently enhances the model's robustness with CIFAR-100 dataset under various adversarial attack settings, achieving up to 28.49
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