Boosting Adversarial Robustness and Generalization with Dictionary Structure

Zhichao Hou, Weizhi Gao, Hamid Krim, Xiaorui Liu

Keywords: Adversarial Robustness, Structural Prior, Adversarial Training

Abstract:

This work investigates a
novel approach to boost adversarial robustness and generalization by incorporating structural prior into the design of deep learning models.
Specifically, our study surprisingly reveals that existing dictionary learning-inspired convolutional neural networks (CNNs) provide a false sense of security against adversarial attacks. To address this, we propose Elastic Dictionary Learning Networks (EDLNets), a novel ResNet architecture that significantly enhances adversarial robustness and generalization.
Extensive and reliable experiments demonstrate consistent and significant performance improvement on open robustness leaderboards such as RobustBench, surpassing state-of-the-art baselines. To the best of our knowledge, this is the first work to discover and validate that
dictionary structure can reliably enhance deep learning robustness under strong adaptive attacks, unveiling a promising direction for future research.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 14223