Enhancing Generalization in Few-Shot Learning for Detecting Unknown Adversarial Examples

Abstract

Deep neural networks, particularly convolutional neural networks, are vulnerable to adversarial examples, undermining their reliability in visual recognition tasks. Adversarial example detection is a crucial defense mechanism against such attacks but often relies on empirical observations and specialized metrics, posing challenges in terms of data efficiency, generalization to unknown attacks, and scalability to high-resolution datasets like ImageNet. To address these issues, we propose a prototypical network-based method using a deep residual network as the backbone architecture. This approach is capable of extracting discriminative features of adversarial and normal examples from various known adversarial examples by constructing few-shot adversarial detection tasks. Then the optimal mapping matrix is computed using the Sinkhorn algorithm from optimal transport theory, and the class centers are iteratively updated, enabling the detection of unknown adversarial examples across scenarios. Experimental results show that the proposed approach outperforms existing methods in the cross-adversary benchmark and achieves enhanced generalization on a subset of ImageNet in detecting both new adversarial attacks and adaptive white-box attacks. The proposed approach offers a promising solution for improving the safety of deep neural networks in practical applications.