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DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformers
In this paper, we present DeSparsify, an attack targeting the availability of vision transformers that use token sparsification mechanisms.
QuantAttack: Exploiting Dynamic Quantization to Attack Vision Transformers
However, their dynamic behavior and average-case performance assumption makes them vulnerable to a novel threat vector -- adversarial attacks that target the model's efficiency and availability.
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection
In this research, we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task.
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