Unsupervised Labelling of Stolen Handwritten Digit Embeddings with Density Matching

International audience; Biometrics authentication is now widely deployed, and from that omnipresence comes the necessity to protect private data. Recent studies proved touchscreen handwritten digits to be a reliable biomet-rics. We set a threat model based on that biometrics: in the event of theft of unlabelled embeddings of handwritten digits, we propose a labelling method inspired by recent unsupervised translation algorithms. Provided a set of unlabelled embeddings known to have been produced by a Long Short Term Memory Recurrent Neural Network (LSTM RNN), we demonstrate that inferring their labels is possible. The proposed approach involves label-wise clustering of the embeddings and label identification of each group by matching their distribution to the label-relative classes of a comparison hand-crafted labeled set of embeddings. Cluster labelling is done through a two steps process including a genetic algorithm that finds the N-best matching hypotheses before a fine-tuning of those N-candidates. The proposed method was able to infer the correct labels on 100 randomised runs on different dataset splits.