An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
Authors: Arash Rahnama, Andrew Tseng | Published: 2020-05-20 | Updated: 2020-09-28
Poisoning
On Intrinsic Dataset Properties for Adversarial Machine Learning
Authors: Jeffrey Z. Pan, Nicholas Zufelt | Published: 2020-05-19
Provable Robust Classification via Learned Smoothed Densities
Authors: Saeed Saremi, Rupesh Srivastava | Published: 2020-05-09
When Machine Unlearning Jeopardizes Privacy
Authors: Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang | Published: 2020-05-05 | Updated: 2021-09-14
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security
Authors: Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram, Armida J. Carbajal, Ramyaa Ramyaa, Evelyn Domschot, Christopher C. Lamb, Stephen J. Verzi, W. Philip Kegelmeyer | Published: 2020-05-04
Depth-2 Neural Networks Under a Data-Poisoning Attack
Authors: Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou | Published: 2020-05-04 | Updated: 2022-06-29
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?
Authors: Marco Melis, Michele Scalas, Ambra Demontis, Davide Maiorca, Battista Biggio, Giorgio Giacinto, Fabio Roli | Published: 2020-05-04 | Updated: 2021-05-27
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability
Authors: Hojjat Aghakhani, Dongyu Meng, Yu-Xiang Wang, Christopher Kruegel, Giovanni Vigna | Published: 2020-05-01 | Updated: 2021-03-14
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Authors: Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin | Published: 2020-04-30 | Updated: 2020-07-03
Bias Busters: Robustifying DL-based Lithographic Hotspot Detectors Against Backdooring Attacks
Authors: Kang Liu, Benjamin Tan, Gaurav Rajavendra Reddy, Siddharth Garg, Yiorgos Makris, Ramesh Karri | Published: 2020-04-26