2 citations · 2 across the 4 of their papers we have counts for
4 papers
Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction
Abhijith Sharma, Phil Munz, Apurva Narayan
Adversarial patches threaten visual AI models in the real world. The number of patches in a patch attack is variable and determines the attack's potency in a specific environment.…
NSA: Naturalistic Support Artifact to Boost Network Confidence
Abhijith Sharma, Phil Munz, Apurva Narayan
Visual AI systems are vulnerable to natural and synthetic physical corruption in the real-world. Such corruption often arises unexpectedly and alters the model's performance. In re…
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey
Abhijith Sharma, Yijun Bian, Phil Munz +1
Adversarial attacks in deep learning models, especially for safety-critical systems, are gaining more and more attention in recent years, due to the lack of trust in the security a…
Soft Adversarial Training Can Retain Natural Accuracy
Abhijith Sharma, Apurva Narayan
Adversarial training for neural networks has been in the limelight in recent years. The advancement in neural network architectures over the last decade has led to significant impr…