Unsupervised Robust Domain Adaptation: Paradigm, Theory and Algorithm

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a label-rich source domain to an unlabeled target domain by addressing domain shifts. Most UDA approaches emphasize transfer ability, but often overlook robustness against adversarial attacks. Although vanilla adversarial training (VAT) improves the robustness of deep neural networks, it has little effect on UDA. This paper focuses on answering three key questions: 1) Why does VAT, known for its defensive effectiveness, fail in the UDA paradigm? 2) What is the generalization bound theory under attacks and how does it evolve from classical UDA theory? 3) How can we implement a robustification training procedure without complex modifications? Specifically, we explore and reveal the inherent entanglement challenge in general UDA+VAT paradigm, and propose an unsupervised robust domain adaptation (URDA) paradigm. We further derive the generalization bound theory of the URDA paradigm so that it can resist adversarial noise and domain shift. To the best of our knowledge, this is the first time to establish the URDA paradigm and theory. We further introduce a simple, novel yet effective URDA algorithm called Disentangled Adversarial Robustness Training (DART), a two-step training procedure that ensures both transferability and robustness. DART first pre-trains an arbitrary UDA model, and then applies an instantaneous robustification post-training step via disentangled distillation.Experiments on four benchmark datasets with/without attacks show that DART effectively enhances robustness while maintaining domain adaptability, and validate the URDA paradigm and theory.

Invariance-powered Trustworthy Defense via Remove Then Restore

Adversarial attacks pose a challenge to the deployment of deep neural networks (DNNs), while previous defense models overlook the generalization to various attacks. Inspired by targeted therapies for cancer, we view adversarial samples as local lesions of natural benign samples, because a key finding is that salient attack in an adversarial sample dominates the attacking process, while trivial attack unexpectedly provides trustworthy evidence for obtaining generalizable robustness. Based on this finding, a Pixel Surgery and Semantic Regeneration (PSSR) model following the targeted therapy mechanism is developed, which has three merits: 1) To remove the salient attack, a score-based Pixel Surgery module is proposed, which retains the trivial attack as a kind of invariance information. 2) To restore the discriminative content, a Semantic Regeneration module based on a conditional alignment extrapolator is proposed, which achieves pixel and semantic consistency. 3) To further harmonize robustness and accuracy, an intractable problem, a self-augmentation regularizer with adversarial R-drop is designed. Experiments on numerous benchmarks show the superiority of PSSR.