20 citations · 20 across the 4 of their papers we have counts for
4 papers
DAD++: Improved Data-free Test Time Adversarial Defense
Gaurav Kumar Nayak, Inder Khatri, Shubham Randive +2
With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness h…
GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization
Vicente Vivanco Cepeda, Gaurav Kumar Nayak, Mubarak Shah
Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. This task has considerable challenges due to immense variation in geographic lan…
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks
Indu Joshi, Priyank Upadhya, Gaurav Kumar Nayak +2
Federated learning is a promising direction to tackle the privacy issues related to sharing patients' sensitive data. Often, federated systems in the medical image analysis domain…
DAD: Data-free Adversarial Defense at Test Time
Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
Deep models are highly susceptible to adversarial attacks. Such attacks are carefully crafted imperceptible noises that can fool the network and can cause severe consequences when…