Deep learning models require large amounts of clean data to acheive good performance. To avoid the cost of expensive data acquisition, researchers use the abundant data available on the internet. This raises significant privacy concerns on the potential misuse of personal data for model training without authorisation. Recent works such as CUDA propose solutions to this problem by adding class-wise blurs to make datasets unlearnable, i.e a model can never use the acquired dataset for learning. However these methods often reduce the quality of the data making it useless for practical applications. We introduce PosCUDA, a position based convolution for creating unlearnable audio datasets. PosCUDA uses class-wise convolutions on small patches of audio. The location of the patches are based on a private key for each class, hence the model learns the relations between positional blurs and labels, while failing to generalize. We empirically show that PosCUDA can achieve unlearnability while maintaining the quality of the original audio datasets. Our proposed method is also robust to different audio feature representations such as MFCC, raw audio and different architectures such as transformers, convolutional networks etc.

翻译：深度学习模型需要大量干净数据以实现良好性能。为避免昂贵的数据采集成本，研究者常利用互联网上丰富的数据资源，但这引发了关于未经授权使用个人数据进行模型训练的隐私担忧。近期如CUDA等研究通过添加类别模糊化使数据集不可学习（即模型无法利用获取的数据集进行学习）提出了解决方案，但这些方法常以降低数据质量使其无法用于实际应用为代价。我们提出PosCUDA——一种基于位置的卷积方法，用于生成不可学习的音频数据集。PosCUDA对音频小片段采用类别级卷积，每个类别片段的位置基于私有密钥确定，从而使模型学习位置模糊与标签之间的关联关系，但无法泛化。实验证明，PosCUDA在保持原始音频数据集质量的同时实现不可学习性。该方法对MFCC、原始音频等不同音频特征表示，以及Transformer、卷积网络等不同架构均具有鲁棒性。