AML-L.1: Adversarial Machine Learning I |
Symposium: Signal Processing for Adversarial Machine Learning |
Session Type: Lecture |
Time: Thursday, November 29, 14:00 - 15:30 |
Location: Castle |
Session Chairs: Sijia Liu, IBM and Pin-Yu Chen, IBM Research AI |
14:00 - 14:18 |
AML-L.1.1: DIFFERENTIALLY PRIVATE SPARSE INVERSE COVARIANCE ESTIMATION |
Di Wang; State University of New York at Buffalo |
Mengdi Huai; State University of New York at Buffalo |
Jinhui Xu; State University of New York at Buffalo |
14:18 - 14:36 |
AML-L.1.2: DEFENDING DNN ADVERSARIAL ATTACKS WITH PRUNING AND LOGITS AUGMENTATION |
Siyue Wang; Northeastern University |
Xiao Wang; Boston University |
Shaokai Ye; Syracuse University |
Pu Zhao; Northeastern University |
Xue Lin; Northeastern University |
14:36 - 14:54 |
AML-L.1.3: ON THE UTILITY OF CONDITIONAL GENERATION BASED MUTUAL INFORMATION FOR CHARACTERIZING ADVERSARIAL SUBSPACES |
Chia-Yi Hsu; National Chung Hsing University |
Pei-Hsuan Lu; National Chung Hsing University |
Pin-Yu Chen; IBM Research AI |
Chia-Mu Yu; National Chung Hsing University |
14:54 - 15:12 |
AML-L.1.4: BACKDOOR ATTACKS ON NEURAL NETWORK OPERATIONS |
Joseph Clements; Clemson University |
Yingjie Lao; Clemson University |
15:12 - 15:30 |
AML-L.1.5: ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM |
Tsui-Wei Weng; Massachusetts Institute of Technology |
Huan Zhang; University of California, Davis |
Pin-Yu Chen; IBM Research AI |
Aurelie Lozano; IBM Research AI |
Cho-Jui Hsieh; University of California, Davis |
Luca Daniel; Massachusetts Institute of Technology |