Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks
Authors: Rémi Bernhard, Pierre-Alain Moellic, Jean-Max Dutertre | Published: 2019-09-27 | Updated: 2020-07-06
敵対的サンプル
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
Authors: Tianyu Pang, Kun Xu, Jun Zhu | Published: 2019-09-25 | Updated: 2020-02-20
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection
Authors: Aminollah Khormali, Ahmed Abusnaina, Songqing Chen, DaeHun Nyang, Aziz Mohaisen | Published: 2019-09-20
Adversarial Vulnerability Bounds for Gaussian Process Classification
Authors: Michael Thomas Smith, Kathrin Grosse, Michael Backes, Mauricio A Alvarez | Published: 2019-09-19
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks
Authors: Sekitoshi Kanai, Yasutoshi Ida, Yasuhiro Fujiwara, Masanori Yamada, Shuichi Adachi | Published: 2019-09-19
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Authors: Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, Anil K. Jain | Published: 2019-09-17 | Updated: 2019-10-09
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges
Authors: Jinyuan Jia, Neil Zhenqiang Gong | Published: 2019-09-17 | Updated: 2019-09-19
Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix
Authors: Chaomin Shen, Yaxin Peng, Guixu Zhang, Jinsong Fan | Published: 2019-09-13
An Empirical Investigation of Randomized Defenses against Adversarial Attacks
Authors: Yannik Potdevin, Dirk Nowotka, Vijay Ganesh | Published: 2019-09-12
Inspecting adversarial examples using the Fisher information
Authors: Jörg Martin, Clemens Elster | Published: 2019-09-12