SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters
Hassan Ali, Faiq Khalid, Hammad Ali Tariq, Muhammad Abdullah Hanif, Rehan Ahmed, Semeen Rehman · IEEE Design and Test · 2019
Training data is crucial in ensuring robust neural inference, and deep neural networks (DNNs) are heavily dependent on this assumption. However, DNNs can be exploited by adversaries that facilitate various attacks. Adversarial defenses include several techniques, some of which happen during the preprocessing stages (i.e., noise filtering, etc.). This article analyzes the impact of some preprocessing filters, and proposes a selective preprocessing method which increases robustness and reduces the computational complexity.