Nonlinear Transformations Against Unlearnable Datasets

Automated scraping stands out as a common method for collecting data in deep learning models without the authorization of data owners. Recent studies have begun to tackle the privacy concerns associated with this data collection method. Notable approaches include Deepconfuse, error-minimizing, error-maximizing (also known as adversarial poisoning), Neural Tangent Generalization Attack, synthetic, autoregressive, One-Pixel Shortcut, Self-Ensemble Protection, Entangled Features, Robust Error-Minimizing, Hypocritical, TensorClog, and Provably Unlearnable Examples. The data generated by those approaches, called \u201cunlearnable\u201d examples, are prevented \u201clearning\u201d by deep learning models. In this research, we investigate and devise an effective nonlinear transformation framework and conduct extensive experiments to demonstrate that a deep neural network can effectively learn from the data\/examples traditionally considered unlearnable produced by the above 13 approaches. The resulting approach improves the ability to break unlearnable data compared to the linear separable technique recently proposed by researchers. Specifically, our extensive experiments show that the improvement ranges from 0.65% to 234.74% for the unlearnable CIFAR10 datasets generated by those 13 data protection approaches, except the One-Pixel Shortcut and Provably Unlearnable Examples. Moreover, the proposed framework achieves over 100% improvement in test accuracy for Autoregressive and REM approaches compared to the linear separable technique. Our findings suggest that these approaches are inadequate in preventing unauthorized uses of data in machine learning models. There is an urgent need to develop more robust protection mechanisms that effectively thwart an attacker from accessing data without proper authorization from the owners