Making Audio Data Unlearnable

In recent years, deep neural networks (DNNs) have driven rapid advancements in various fields. As DNNs continue to grow in size, the amount of training data required is also increasing. Many researchers crawl publicly available data from the internet for training, which raises issues of unauthorized exploitation and potential privacy leakage. Recent work against unauthorized exploitation primarily focuses on the image domain, while the audio domain remains underexplored. In this paper, we propose an effective method to generate audio unlearnable examples, which injects imperceptible perturbations into training samples, making them unlearnable for models to train. Specifically, we employ an error minimization optimization algorithm to iteratively optimize the generated perturbations. To ensure these perturbations remain imperceptible, we leverage both the Short-Time Objective Intelligibility (STOI) score and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{2}$</tex> norm as measures to constrain the perceptibility of the unlearnable examples. To further improve the transferability of the unlearnable effects, we use an ensemble model and threshold constraint to effectively enhance generated unlearnable examples. Experimental results demonstrate that our method effectively generates samples that prevent models from accurately learning and making predictions, while remaining indistinguishable from clean samples to human observers.