FADER: Fast adversarial example rejection

• Published in: Neurocomputing (Amsterdam) 2022-01, Vol.470, p.257-268
• Main Authors: Crecchi, Francesco, Melis, Marco, Sotgiu, Angelo, Bacciu, Davide, Biggio, Battista
• Format: Article
• Language: English
• Subjects: Adversarial examples; Adversarial machine learning; Deep learning; Detection; Evasion attacks; RBF networks
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2021.10.082

Deep neural networks are vulnerable to adversarial examples, i.e., carefully-crafted inputs that mislead classification at test time. Recent defenses have been shown to improve adversarial robustness by detecting anomalous deviations from legitimate training samples at different layer representations - a behavior normally exhibited by adversarial attacks. Despite technical differences, all aforementioned methods share a common backbone structure that we formalize and highlight in this contribution, as it can help in identifying promising research directions and drawbacks of existing methods. The first main contribution of this work is the review of these detection methods in the form of a unifying framework designed to accommodate both existing defenses and newer ones to come. In terms of drawbacks, the overmentioned defenses require comparing input samples against an oversized number of reference prototypes, possibly at different representation layers, dramatically worsening the test-time efficiency. Besides, such defenses are typically based on ensembling classifiers with heuristic methods, rather than optimizing the whole architecture in an end-to-end manner to better perform detection. As a second main contribution of this work, we introduce FADER, a novel technique for speeding up detection-based methods. FADER overcome the issues above by employing RBF networks as detectors: by fixing the number of required prototypes, the runtime complexity of adversarial examples detectors can be controlled. Our experiments outline up to 73× prototypes reduction compared to analyzed detectors for MNIST dataset, up to 50× for CIFAR10 dataset, and up to 82× on ImageNet10 dataset respectively, without sacrificing classification accuracy on both clean and adversarial data.