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 …

In an era of widespread web scraping, unlearnable dataset methods have the potential to
protect data privacy by preventing deep neural networks from generalizing. But in addition to …

Unlearnable example attacks are data poisoning techniques that can be used to safeguard
public data against unauthorized use for training deep learning models. These methods add …

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 score-based query attacks (SQAs) pose practical threats to deep neural networks by
crafting adversarial perturbations within dozens of queries, only using the model's output …

As data becomes increasingly vital, a company would be very cautious about releasing data,
because the competitors could use it to train high-performance models, thereby posing a …

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 …

Unlearnable datasets lead to a drastic drop in the generalization performance of models
trained on them by introducing elaborate and imperceptible perturbations into clean training …