Unlearnable Examples: Making Personal Data Unexploitable (2101.04898v2)

Abstract: The volume of "free" data on the internet has been key to the current success of deep learning. However, it also raises privacy concerns about the unauthorized exploitation of personal data for training commercial models. It is thus crucial to develop methods to prevent unauthorized data exploitation. This paper raises the question: \emph{can data be made unlearnable for deep learning models?} We present a type of \emph{error-minimizing} noise that can indeed make training examples unlearnable. Error-minimizing noise is intentionally generated to reduce the error of one or more of the training example(s) close to zero, which can trick the model into believing there is "nothing" to learn from these example(s). The noise is restricted to be imperceptible to human eyes, and thus does not affect normal data utility. We empirically verify the effectiveness of error-minimizing noise in both sample-wise and class-wise forms. We also demonstrate its flexibility under extensive experimental settings and practicability in a case study of face recognition. Our work establishes an important first step towards making personal data unexploitable to deep learning models.

• The paper’s main contribution is the development of imperceptible error-minimizing noise that renders personal data unlearnable by neural networks.
• The methodology applies both sample-wise and class-wise noise, significantly reducing model accuracy—often to near random guessing levels—across image datasets like CIFAR and ImageNet.
• Experimental findings reveal that the noise withstands common data augmentations and transfers across datasets, highlighting its potential for real-world privacy protection, including in face recognition.

Unlearnable Examples: Making Personal Data Unexploitable

The paper "Unlearnable Examples: Making Personal Data Unexploitable," authored by Huang et al., tackles the imperative concern of data privacy in the age of deep learning dominance. The paper's focal point is an innovative approach to safeguard personal data from unauthorized use in training deep learning models. The authors present a methodology for embedding imperceptible noise into data, rendering it unlearnable by neural networks, thereby offering a potential line of defense against privacy intrusions.

A pivotal aspect of the work is the concept of error-minimizing noise, designed to minimize the training error of models on affected examples, tricking them into perceiving nothing worthwhile to learn. The noise is crafted to be imperceptible to human observers, thus maintaining data utility for purposes other than model training. The research explores two forms of noise application: sample-wise and class-wise. Sample-wise noise applies unique perturbations to each data sample, while class-wise noise applies a uniform perturbation across all samples of a given class.

The experimental results are extensive and robust, demonstrating the efficacy of error-minimizing noise across four popular image datasets: SVHN, CIFAR-10, CIFAR-100, and a subset of ImageNet. The results indicate that models trained on unlearnable examples perform substantially worse on clean test datasets, with accuracy reduced significantly below baseline levels—sometimes near random guessing, especially with class-wise noise. This highlights the potential of error-minimizing noise to serve as a viable tool for personal data protection in the context of image data.

The paper also explores the stability and transferability of unlearnable examples. It is found that effectiveness drops when not all data is made unlearnable, yet in cases where certain classes or subsets of data are protected, the affected classes remain inadequately learned by models. Additionally, the error-minimizing noise showcases resistance to common data augmentation techniques, although adversarial training strategies can mitigate its impact to some extent.

Furthermore, the research investigates the transferability of this defense mechanism, demonstrating that error-minimizing noise generated on one dataset can be applied to another, as seen with successful cross-dataset noise application from ImageNet to CIFAR-10. This finding underscores the practical applicability of the approach for protecting data contributions to larger databases, often constructed without explicit user consent.

In a practical real-world application, the authors apply their methodology to a face recognition scenario, highlighting the noise's potential in protecting personal images shared on social media from being misused for unauthorized facial recognition model training. While completely unlearnable, once shielded, all individuals were difficult to recognize by models trained even with partial unlearning datasets.

The paper's contributions to the field are numerous. By introducing a scalable, imperceptible method of data protection, the work addresses critical concerns related to privacy and unauthorized data exploitation in deep learning. The insights around error-minimizing noise present a fresh direction for privacy-centric machine learning research. As data continues to become an ever valuable resource, the implications of such advancements are profound, offering new layers of control to data ownership in a digital world. Future research could focus on refining these techniques, extending their applicability across data types, and mitigating adversarial training defenses against unlearnable noise.