  Availability attacks can prevent the unauthorized use of private data and
commercial datasets by generating imperceptible noise and making unlearnable
examples before release. Ideally, the obtained unlearnability prevents
algorithms from training usable models. When supervised learning (SL)
algorithms have failed, a malicious data collector possibly resorts to
contrastive learning (CL) algorithms to bypass the protection. Through
evaluation, we have found that most of the existing methods are unable to
achieve both supervised and contrastive unlearnability, which poses risks to
data protection. Different from recent methods based on contrastive error
minimization, we employ contrastive-like data augmentations in supervised error
minimization or maximization frameworks to obtain attacks effective for both SL
and CL. Our proposed AUE and AAP attacks achieve state-of-the-art worst-case
unlearnability across SL and CL algorithms with less computation consumption,
showcasing prospects in real-world applications.
