Developing Hessian-free second-order adversarial examples for adversarial training - Qian, Yaguan; Zhang, Liangjian; Wang, Yugi; Ji, Boyuan; Yao, Tengteng; Wang, Bin (Zielona Góra: Uniwersytet Zielonogórski)
Recent studies show that deep neural networks (DNNs) are extremely vulnerable to elaborately designed adversarial examples. Adversarial training, which uses adversarial examples as training data, has been proven to be one of the most effective methods of defense against adversarial attacks. However, most existing adversarial training methods use adversarial examples relying on first-order gradients, which perform poorly against second-order adversarial attacks and make it difficult to further improve the robustness of the model.
Keywords: adversarial examples; adversarial machine learning; Krylov subspace; deep neural networks