Cross-age Identity Membership Inference Based on Attention Feature Decomposition

Generative adversarial networks(GANs) can generate high-resolution “non-existent” realistic images,so they are widely used in various artificial data synthesis scenarios,especially in the field of face image generation.However,the face generators based on these models typically require highly sensitive facial images of different identities for training,which may lead to potential data leakage enabling attackers to infer identity membership relationships.To address this issue,this study proposes an identity membership inference attack when significant difference exist between the obtained samples and the actual training samples for the queried identity,resulting in a drastic decline in the performance of identity membership inference based on samples.Subsequently,a reconstruction error attack scheme is designed based on attention feature decomposition to further enhance the attack performance.This scheme maximizes the elimination of influences from factors such as background poses between different samples,as well as mitigates the representation difference caused by a large age span.Extensive experiments are conducted on three representative face datasets,training generative models with three mainstream GAN architectures and performing the proposed attacks.Experimental results demonstrate that the proposed attack scheme achieves an average increase of 0.2 in AUCROC value compared to previous researches.