Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks
Authors: Mahdieh Abbasi, Arezoo Rajabi, Christian Gagne, Rakesh B. Bobba | Published: 2020-05-17
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking
Authors: Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal | Published: 2020-05-17 | Updated: 2021-03-31
Universal Adversarial Perturbations: A Survey
Authors: Ashutosh Chaubey, Nikhil Agrawal, Kavya Barnwal, Keerat K. Guliani, Pramod Mehta | Published: 2020-05-16
NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips
Authors: Valerio Venceslai, Alberto Marchisio, Ihsen Alouani, Maurizio Martina, Muhammad Shafique | Published: 2020-05-16
Encryption Inspired Adversarial Defense for Visual Classification
Authors: MaungMaung AprilPyone, Hitoshi Kiya | Published: 2020-05-16
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data
Authors: Deepesh Data, Suhas Diggavi | Published: 2020-05-16
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness
Authors: Tianhang Zheng, Di Wang, Baochun Li, Jinhui Xu | Published: 2020-05-15 | Updated: 2020-06-07
A Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification
Authors: Ahmed Abusnaina, Mohammed Abuhamad, Hisham Alasmary, Afsah Anwar, Rhongho Jang, Saeed Salem, DaeHun Nyang, David Mohaisen | Published: 2020-05-14 | Updated: 2020-05-15
Protecting the integrity of the training procedure of neural networks
Authors: Christian Berghoff | Published: 2020-05-14
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
Authors: Pieter Delobelle, Paul Temple, Gilles Perrouin, Benoît Frénay, Patrick Heymans, Bettina Berendt | Published: 2020-05-14 | Updated: 2020-09-01