Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis
Authors: William Briguglio, Sherif Saad | Published: 2020-01-27
ロバスト性向上手法
On Evaluating Adversarial Robustness
Authors: Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, Alexey Kurakin | Published: 2019-02-18 | Updated: 2019-02-20
Discretization based Solutions for Secure Machine Learning against Adversarial Attacks
Authors: Priyadarshini Panda, Indranil Chakraborty, Kaushik Roy | Published: 2019-02-08 | Updated: 2019-02-11
A Training-based Identification Approach to VIN Adversarial Examples
Authors: Yingdi Wang, Wenjia Niu, Tong Chen, Yingxiao Xiang, Jingjing Liu, Gang Li, Jiqiang Liu | Published: 2018-10-18
ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks
Authors: Mahdi Ahmadi, Alireza Norouzi, S. M. Reza Soroushmehr, Nader Karimi, Kayvan Najarian, Shadrokh Samavi, Ali Emami | Published: 2018-10-16 | Updated: 2018-12-11
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Authors: Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras | Published: 2018-10-16 | Updated: 2019-05-09
Adversarial Examples – A Complete Characterisation of the Phenomenon
Authors: Alexandru Constantin Serban, Erik Poll, Joost Visser | Published: 2018-10-02 | Updated: 2019-02-17
Improving the Generalization of Adversarial Training with Domain Adaptation
Authors: Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft | Published: 2018-10-01 | Updated: 2019-03-15
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
Authors: Xuanqing Liu, Yao Li, Chongruo Wu, Cho-Jui Hsieh | Published: 2018-10-01 | Updated: 2019-05-04
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Authors: Kenneth T. Co, Luis Muñoz-González, Sixte de Maupeou, Emil C. Lupu | Published: 2018-09-30 | Updated: 2019-11-23