37 citations · 110 across the 9 of their papers we have counts for
6 papers · 1 filter
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches
Aniruddha Saha, Shuhua Yu, Arash Norouzzadeh +2
Certifiably robust defenses against adversarial patches for image classifiers ensure correct prediction against any changes to a constrained neighborhood of pixels. PatchCleanser a…
Backdoor Attacks on Vision Transformers
Akshayvarun Subramanya, Aniruddha Saha, Soroush Abbasi Koohpayegani +2
Vision Transformers (ViT) have recently demonstrated exemplary performance on a variety of vision tasks and are being used as an alternative to CNNs. Their design is based on a sel…
Backdoor Attacks on Self-Supervised Learning
Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani +1
Large-scale unlabeled data has spurred recent progress in self-supervised learning methods that learn rich visual representations. State-of-the-art self-supervised methods for lear…
Role of Spatial Context in Adversarial Robustness for Object Detection
Aniruddha Saha, Akshayvarun Subramanya, Koninika Patil +1
The benefits of utilizing spatial context in fast object detection algorithms have been studied extensively. Detectors increase inference speed by doing a single forward pass per i…
Hidden Trigger Backdoor Attacks
Aniruddha Saha, Akshayvarun Subramanya, Hamed Pirsiavash
With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topi…
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash +1
The unprecedented success of deep neural networks in many applications has made these networks a prime target for adversarial exploitation. In this paper, we introduce a benchmark…