Self-supervised learning in computer vision trains on unlabeled data, such as images or
(image, text) pairs, to obtain an image encoder that learns high-quality embeddings for input …

Object detection plays a key role in many security-critical systems. Adversarial patch attacks,
which are easy to implement in the physical world, pose a serious threat to state-of-the-art …

Localized adversarial patches aim to induce misclassification in machine learning models
by arbitrarily modifying pixels within a restricted region of an image. Such attacks can be …

The adversarial patch attack against image classification models aims to inject adversarially
crafted pixels within a restricted image region (ie, a patch) for inducing model …

Crowd counting, which has been widely adopted for estimating the number of people in
safety-critical scenes, is shown to be vulnerable to adversarial examples in the physical …

State-of-the-art object detectors are vulnerable to localized patch hiding attacks, where an
adversary introduces a small adversarial patch to make detectors miss the detection of …

Diffusion models (DM) have become state-of-the-art generative models because of their
capability of generating high-quality images from noises without adversarial training …

Adversarial attacks in deep learning models, especially for safety-critical systems, are
gaining more and more attention in recent years, due to the lack of trust in the security and …

Abstract Machine learning models are known to be susceptible to adversarial perturbation.
One famous attack is the adversarial patch, a particularly crafted sticker that makes the …

An adversarial patch can arbitrarily manipulate image pixels within a restricted region to
induce model misclassification. The threat of this localized attack has gained significant …