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Subject [BMVC 2020] Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack (by Hakmin Lee and Hong Joo Lee) is accepted in BMVC 2020
Name IVY Lab. KAIST
Date 2020-07-31
Title: Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack
Authors: Hakmin Lee*, Hong Joo Lee*, Seong Tae Kim, and Yong Man Ro
* Both authors contributed equally to this work.

Deep neural networks have achieved substantial achievements in several computer vision areas, but have vulnerabilities that are often fooled by adversarial examples that are not recognized by humans. This is an important issue for security or medical applications. In this paper, we propose an ensemble model training framework with random layer sampling to improve the robustness of deep neural networks. In the proposed training framework, we generate various sampled model through the random layer sampling and update the weight of the sampled model. After the ensemble models are trained, it can hide the gradient efficiently and avoid the gradient-based attack by the random layer sampling method. To evaluate our proposed method, comprehensive and comparative experiment have been conducted on three datasets. Experimental results show that the proposed method improves the adversarial robustness.