Perturbative availability poisoning (PAP) adds small changes to images to prevent their use
for model training. Current research adopts the belief that practical and effective approaches …

With more people publishing their personal data online, unauthorized data usage has
become a serious concern. The unlearnable strategies have been introduced to prevent …

There is a growing interest in developing unlearnable examples (UEs) against visual privacy
leaks on the Internet. UEs are training samples added with invisible but unlearnable noise …

Safeguarding data from unauthorized exploitation is vital for privacy and security, especially
in recent rampant research in security breach such as adversarial/membership attacks. To …

The efficacy of availability poisoning, a method of poisoning data by injecting imperceptible
perturbations to prevent its use in model training, has been a hot subject of investigation …

Privacy preserving has become increasingly critical with the emergence of social media.
Unlearnable examples have been proposed to avoid leaking personal information on the …

Ensuring data privacy and protection has become paramount in the era of deep learning.
Unlearnable examples are proposed to mislead the deep learning models and prevent data …

Multimodal contrastive learning (MCL) has shown remarkable advances in zero-shot
classification by learning from millions of image-caption pairs crawled from the Internet …

As Graph Neural Networks (GNNs) become increasingly prevalent in a variety of fields, from
social network analysis to protein-protein interaction studies, growing concerns have …

We consider availability data poisoning attacks, where an adversary aims to degrade the
overall test accuracy of a machine learning model by crafting small perturbations to its …