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Found 7 papers, 2 papers with code
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.
Attacking Object Detector Using A Universal Targeted Label-Switch Patch
However, none of prior research proposed a misclassification attack on ODs, in which the patch is applied on the target object.
Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors
Adversarial attacks against deep learning-based object detectors have been studied extensively in the past few years.
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model
In our experiments, we examined the transferability of our adversarial mask to a wide range of FR model architectures and datasets.
The Translucent Patch: A Physical and Universal Attack on Object Detectors
Therefore, in our experiments, which are conducted on state-of-the-art object detection models used in autonomous driving, we study the effect of the patch on the detection of both the selected target class and the other classes.
