  Transfer learning is a popular method for tuning pretrained (upstream) models
for different downstream tasks using limited data and computational resources.
We study how an adversary with control over an upstream model used in transfer
learning can conduct property inference attacks on a victim's tuned downstream
model. For example, to infer the presence of images of a specific individual in
the downstream training set. We demonstrate attacks in which an adversary can
manipulate the upstream model to conduct highly effective and specific property
inference attacks (AUC score $> 0.9$), without incurring significant
performance loss on the main task. The main idea of the manipulation is to make
the upstream model generate activations (intermediate features) with different
distributions for samples with and without a target property, thus enabling the
adversary to distinguish easily between downstream models trained with and
without training examples that have the target property. Our code is available
at https://github.com/yulongt23/Transfer-Inference.

Machine Learning;Artificial Intelligence;Cryptography and Security