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Safeguarding data from unauthorized exploitation is vital for privacy andsecurity, especially in recent rampant research in security breach such asadversarial/membership attacks. To this end, \textit{unlearnable examples}(UEs) have been recently proposed as a compelling protection, by addingimperceptible perturbation to data so that models trained on them cannotclassify them accurately on original clean distribution. Unfortunately, we findUEs provide a false sense of security, because they cannot stop unauthorizedusers from utilizing other unprotected data to remove the protection, byturning unlearnable data into learnable again. Motivated by this observation,we formally define a new threat by introducing \textit{learnable unauthorizedexamples} (LEs) which are UEs with their protection removed. The core of thisapproach is a novel purification process that projects UEs onto the manifold ofLEs. This is realized by a new joint-conditional diffusion model which denoisesUEs conditioned on the pixel and perceptual similarity between UEs and LEs.Extensive experiments demonstrate that LE delivers state-of-the-art counteringperformance against both supervised UEs and unsupervised UEs in variousscenarios, which is the first generalizable countermeasure to UEs acrosssupervised learning and unsupervised learning. Our code is available at\url{https://github.com/jiangw-0/LE_JCDP}.

Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examples