PosCUDA: Position based Convolution for Unlearnable Audio Datasets
Vignesh Gokul Shlomo Dubnov
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.