LFGN: Low-Level Feature-Guided Network For Adversarial Defense

Abstract

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Adversarial attacks cause deep learning models to fail, which presents a significant challenge in the field. Consequently, the development of adversarial defense techniques has become crucial. Current defense strategies struggle to effectively address adversarial attacks, making a robust defense strategy highly desirable. State-of-the-art adversarial defense schemes mainly rely on adversarial training, which requires massive computational resources. Another strategy, the transformbased approach, is a faster and more efficient way for robust model design. The current state-of-the-art method, Deepimage-prior-based (DIP), requires online training, making fast inference impossible. This paper proposes a novel learning pipeline incorporating conventional low-level features as the transform for fast inference and achieving state-of-the-art performance for adversarial defense. First, we discover the feature transformation for reducing the impact of adversarial attacks since it is hard to approximate using gradients. Conventional low-level feature extraction, such as local binary and ternary patterns, perfectly fits this requirement, allowing us to combine moderate deep neural networks with traditional low-level features for adversarial defense, which could easily be extended to existing defense methods. We conduct comprehensive experiments and analyses to demonstrate the superiority of the proposed adversarial defense scheme and achieve the best trade-off between performance and efficiency in real-world defense scenarios.