9 papers
A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization
Anadi Goyal, Nandish Chattopadhyay, Anupam Chattopadhyay +1
Vision Transformers (ViTs) increasingly rely on input-adaptive inference, such as token pruning and early halting, to meet energy and latency budgets. This survey examines a recent…
MOAT: Model-Agnostic Randomized Transformations for preventing Efficiency Degradation Attacks on ViTs
Anadi Goyal, Nandish Chattopadhyay, Chandan Karfa +2
To adopt the Vision Transformers (ViTs) in resource-constrained environment, token pruning is widely used to reduce computational cost without impacting accuracy. However, adversar…
STRAP-ViT: Segregated Tokens with Randomized -- Transformations for Defense against Adversarial Patches in ViTs
Nandish Chattopadhyay, Anadi Goyal, Chandan Karfa +1
Adversarial patches are physically realizable localized noise, which are able to hijack Vision Transformers (ViT) self-attention, pulling focus toward a small, high-contrast region…
PatchBlock: A Lightweight Defense Against Adversarial Patches for Embedded EdgeAI Devices
Nandish Chattopadhyay, Abdul Basit, Amira Guesmi +3
Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous driving and surveillance, which re…
Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation
Anh Tu Ngo, Chuan Song Heng, Nandish Chattopadhyay +1
Deep Neural Networks (DNNs) have gained considerable traction in recent years due to the unparalleled results they gathered. However, the cost behind training such sophisticated mo…
A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges
Nandish Chattopadhyay, Abdul Basit, Bassem Ouni +1
Adversarial attacks have emerged as a major challenge to the trustworthy deployment of machine learning models, particularly in computer vision applications. These attacks have a v…