FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries

Understand

Transfer learning is a useful machine learning framework that allows one to build task-specific models (student models) without significantly incurring training costs using a single powerful model (teacher model) pre-trained with a large amount of data.

• The teacher model may contain private data, or interact with private inputs.
• We investigate if one can leak or infer such private information without interacting with the teacher model directly.
• We describe such inference attacks in the context of face recognition, an application of transfer learning that is highly sensitive to personal privacy.