Confusing and Detecting ML Adversarial Attacks with Injected Attractors
Authors: Jiyi Zhang, Ee-Chien Chang, Hwee Kuan Lee | Published: 2020-03-05 | Updated: 2021-03-08
透かし技術
Detecting and Correcting Adversarial Images Using Image Processing Operations
Authors: Huy H. Nguyen, Minoru Kuribayashi, Junichi Yamagishi, Isao Echizen | Published: 2019-12-11 | Updated: 2019-12-30
Robustness properties of Facebook’s ResNeXt WSL models
Authors: A. Emin Orhan | Published: 2019-07-17 | Updated: 2019-12-09
DAWN: Dynamic Adversarial Watermarking of Neural Networks
Authors: Sebastian Szyller, Buse Gul Atli, Samuel Marchal, N. Asokan | Published: 2019-06-03 | Updated: 2021-07-16
TensorSCONE: A Secure TensorFlow Framework using Intel SGX
Authors: Roland Kunkel, Do Le Quoc, Franz Gregor, Sergei Arnautov, Pramod Bhatotia, Christof Fetzer | Published: 2019-02-12
How Robust are Deep Neural Networks?
Authors: Biswa Sengupta, Karl J. Friston | Published: 2018-04-30
t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification
Authors: Tomi Kinnunen, Kong Aik Lee, Hector Delgado, Nicholas Evans, Massimiliano Todisco, Md Sahidullah, Junichi Yamagishi, Douglas A. Reynolds | Published: 2018-04-25 | Updated: 2019-04-11
Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size
Authors: Ian Goodfellow | Published: 2018-04-21
Learning More Robust Features with Adversarial Training
Authors: Shuangtao Li, Yuanke Chen, Yanlin Peng, Lin Bai | Published: 2018-04-20
On the Limitation of MagNet Defense against $L_1$-based Adversarial Examples
Authors: Pei-Hsuan Lu, Pin-Yu Chen, Kang-Cheng Chen, Chia-Mu Yu | Published: 2018-04-14 | Updated: 2018-05-09