Nonlinear Transformations Against Unlearnable Datasets

TL;DR

This paper introduces a nonlinear transformation framework that significantly improves the ability of deep neural networks to learn from datasets previously considered unlearnable, challenging existing data protection methods.

Contribution

The study proposes a novel nonlinear transformation approach that outperforms linear techniques in breaking unlearnable datasets created by various data protection strategies.

Findings

Improved learning accuracy on unlearnable CIFAR10 datasets by up to 249.59%.

Achieved over 100% improvement for Autoregressive and REM approaches.

Demonstrated that current unlearnable data methods are insufficient for data protection.

Abstract

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, and TensorClog. The data generated by those approaches, called "unlearnable" examples, are prevented "learning" 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 twelve 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 0.34% to 249.59% for the unlearnable CIFAR10 datasets generated by those twelve data protection approaches, except for One-Pixel Shortcut. Moreover, the proposed framework achieves over 100% improvement of 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.