Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks
随机对数缩放：防御深度神经网络对抗基于黑盒分数的对抗样本攻击
机构 * Sharif University of Technology（伊朗谢里夫理工大学）
AI总结 研究针对基于黑盒分数的对抗样本攻击的防御与攻击方法，提出随机对数缩放（RLS）防御可降低攻击成功率，还引入新型自适应攻击，证明一种非随机化黑盒防御易受攻击。
Comments Accepted at Transactions on Machine Learning Research (TMLR)
