8 citations · 17 across the 13 of their papers we have counts for
4 papers · 1 filter
Robust Few-shot Learning Without Using any Adversarial Samples
Gaurav Kumar Nayak, Ruchit Rawal, Inder Khatri +1
The high cost of acquiring and annotating samples has made the `few-shot' learning problem of prime importance. Existing works mainly focus on improving performance on clean data a…
Data-free Defense of Black Box Models Against Adversarial Attacks
Gaurav Kumar Nayak, Inder Khatri, Ruchit Rawal +1
Several companies often safeguard their trained deep models (i.e., details of architecture, learnt weights, training details etc.) from third-party users by exposing them only as b…
DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers
Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
Certified defense using randomized smoothing is a popular technique to provide robustness guarantees for deep neural networks against l2 adversarial attacks. Existing works use thi…
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems
Gaurav Kumar Nayak, Ruchit Rawal, Rohit Lal +2
Adversarial attack perturbs an image with an imperceptible noise, leading to incorrect model prediction. Recently, a few works showed inherent bias associated with such attack (rob…