Robustification of Deep Net Classifiers by Key Based Diversified Aggregation with Pre-Filtering

• Main Authors: Taran, Olga, Rezaeifar, Shideh, Holotyak, Taras, Voloshynovskiy, Slava
• Format: Conference Proceeding
• Language: English
• Subjects: Adversarial attacks; black / gray-box; Classification algorithms; Computer architecture; defense; Discrete cosine transforms; machine learning; Microsoft Windows; non-gradient / gradient based attacks; Perturbation methods; Training
• ISSN: 2381-8549
• DOI: 10.1109/ICIP.2019.8803714

In this paper, we address a problem of machine learning system vulnerability to adversarial attacks. We propose and investigate a Key based Diversified Aggregation (KDA) mechanism as a defense strategy. The KDA assumes that the attacker (i) knows the architecture of classifier and the used de-fense strategy, (ii) has an access to the training data set but (iii) does not know the secret key. The robustness of the system is achieved by a specially designed key based randomization. The proposed randomization prevents the gradients' back propagation or the creating of a "bypass" system. The randomization is performed simultaneously in several channels and a multi-channel aggregation stabilizes the results of randomization by aggregating soft outputs from each classifier in multi-channel system. The performed experimental evaluation demonstrates a high robustness and universality of the KDA against the most efficient gradient based attacks like those proposed by N. Carlini and D. Wagner [1] and the non-gradient based sparse adversarial perturbations like OnePixel attacks [2].