3 papers
cs.CR2026
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…
cs.CR2026
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…
cs.CV2026
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…