1.8k citations · 1.8k across the 4 of their papers we have counts for
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Gradient Based Activations for Accurate Bias-Free Learning
Vinod K Kurmi, Rishabh Sharma, Yash Vardhan Sharma +1
Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial lear…
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui +1
We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundar…
CAAD 2018: Generating Transferable Adversarial Examples
Yash Sharma, Tien-Dung Le, Moustafa Alzantot
Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations carefully crafted to fool the targeted DNN, in both the non-targeted and targeted case. In the non…
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty +3
Deep neural networks are vulnerable to adversarial examples, even in the black-box setting, where the attacker is restricted solely to query access. Existing black-box approaches t…
Are Generative Classifiers More Robust to Adversarial Attacks?
Yingzhen Li, John Bradshaw, Yash Sharma
There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively develo…