Membership Inference Attack Using Self Influence Functions

Member inference (MI) attacks aim to determine if a specific data sample was used to train a machine learning model. Thus, MI is a major privacy threat to models trained on private sensitive data, such as medical records. In MI attacks one may consider the black-box settings, where the model's parameters and activations are hidden from the adversary, or the white-box case where they are available to the attacker. In this work, we focus on the latter and present a novel MI attack for it that employs influence functions, or more specifically the samples' self-influence scores, to perform MI prediction. The proposed method is evaluated on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets using various architectures such as AlexNet, ResNet, and DenseNet. Our new attack method achieves new state-of-the-art (SOTA) results for MI even with limited adversarial knowledge, and is effective against MI defense methods such as data augmentation and differential privacy. Our code is available at https: //github.com/giladcohen/sif_mi_attack.