7 citations · 8 across the 3 of their papers we have counts for
4 papers
It Takes Little to Rewrite Perception: Targeted Semantic Substitution in Vision-Language Models at
Binchi Zhang, Atrisha Sarkar, Apurva Narayan
Vision Language Models (VLMs) are widely deployed in safety-critical scenarios, and understanding to which extent they can be controlled by adversarial perturbation is a prerequisi…
Filtered-ViT: A Robust Defense Against Multiple Adversarial Patch Attacks
Aja Khanal, Ahmed Faid, Apurva Narayan
Deep learning vision systems are increasingly deployed in safety-critical domains such as healthcare, yet they remain vulnerable to small adversarial patches that can trigger miscl…
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks
Yohannis Kifle Telila, Damitha Senevirathne, Dumindu Tissera +3
Anomaly detection is crucial in the energy sector to identify irregular patterns indicating equipment failures, energy theft, or other issues. Machine learning techniques for anoma…
Generating Universal Adversarial Perturbations for Quantum Classifiers
Gautham Anil, Vishnu Vinod, Apurva Narayan
Quantum Machine Learning (QML) has emerged as a promising field of research, aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodolo…