Advancing Adversarial Training by Injecting Booster Signal
Hong Joo Lee Youngjoon Yu Yong Man Ro
Recent works have demonstrated that deep neural networks (DNNs) are highly
vulnerable to adversarial attacks. To defend against adversarial attacks, many
defense strategies have been proposed, among which adversarial training has
been demonstrated to be the most effective strategy. However, it has been known
that adversarial training sometimes hurts natural accuracy. Then, many works
focus on optimizing model parameters to handle the problem. Different from the
previous approaches, in this paper, we propose a new approach to improve the
adversarial robustness by using an external signal rather than model
parameters. In the proposed method, a well-optimized universal external signal
called a booster signal is injected into the outside of the image which does
not overlap with the original content. Then, it boosts both adversarial
robustness and natural accuracy. The booster signal is optimized in parallel to
model parameters step by step collaboratively. Experimental results show that
the booster signal can improve both the natural and robust accuracies over the
recent state-of-the-art adversarial training methods. Also, optimizing the
booster signal is general and flexible enough to be adopted on any existing
adversarial training methods.