How Far Are We from True Unlearnability?
Keywords: shortcut learning, unlearnable dataset, loss landscape
TL;DR: The first to reveal and quantify the nature of unlearnability from the perspective of parameter learning.
Abstract: High-quality data plays an indispensable role in the era of large models, but the use of unauthorized data for model training greatly damages the interests of data owners. To overcome this threat, several unlearnable methods have been proposed, which generate unlearnable examples (UEs) by compromising the training availability of data. Clearly, due to unknown training purpose and the powerful representation learning capabilities of existing models, these data are expected to be unlearnable for various task models, i.e., they will not help improve the model's performance. However, unexpectedly, we find that on the multi-task dataset Taskonomy, UEs still perform well in tasks such as semantic segmentation, failing to exhibit cross-task unlearnability. This phenomenon leads us to question: How far are we from attaining truly unlearnable examples? We attempt to answer this question from the perspective of model optimization. We observe the difference of convergence process between clean models and poisoned models on a simple model using the loss landscape and find that only a part of the critical parameter optimization paths show significant differences, implying a close relationship between the loss landscape and unlearnability. Consequently, we employ the loss landscape to explain the underlying reasons for UEs and propose Sharpness-Aware Learnability (SAL) for quantifying the unlearnability of parameters based on this explanation. Furthermore, we propose an Unlearnable Distance (UD) metric to measure the unlearnability of data based on the SAL distribution of parameters in clean and poisoned models. Finally, we conduct benchmark tests on mainstream unlearnable methods using the proposed UD, aiming to promote community awareness of the capability boundaries of existing unlearnable methods. The code is available at https://github.com/MLsecurityLab/HowFarAreFromTrueUnlearnability.git.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 7533
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