Attack-Aware Detection and Defense to Resist Adversarial Examples

摘要

This article approaches to design an attack-aware detection and defense framework to resist adversarial attacks on the security-critical artificial intelligent systems. We first make efforts to test the performances of adversarial attacks and present classifying and grading rule (CGR) for the fine-grained grouping of adversarial example attacks. According to CGR, adversarial attacks can be divided into six groups. Then, we propose a feature squeezing and CGR-based detector to detect adversarial attacks, which can be aware of the detailed attack group and is evaluated to be effective by extensive experiments. We also test the defense performances of typical defense methods against these six groups of adversarial attacks, and finally give the defense recommendations for each type of adversarial attack.