Digital Watermarking For Protecting Audio Classification Datasets

In this study, we investigate the possibility of protecting audio classification datasets used in deep learning by embedding a pattern in the magnitude of the time-frequency representation of a subset of the dataset. Previous studies on audio watermarking technologies require the actual sound of the watermarked audio to extract the information embedded in it. In our study, we propose an audio watermarking framework aimed to identify whether a deep learning based audio classification model is trained with the watermarked audio classification dataset or not by using only the classification results. The experimental results show that our proposed method can identify the usage of an audio classification dataset while having minimal effect on the overall classification performance. The results are consistent with three different audio classification datasets. The proposed method is robust to different types and parameters of time-frequency representations and classification models.