Model-Free Template Reconstruction Attack with Feature Converter

Abstract

State-of-the-art template reconstruction attacks assume that an adversary has access to a part or whole of the functionality of a target model. However, in a practical scenario, rigid protection of the target system prevents them from gaining knowledge of the target model. In this paper, we propose a novel template reconstruction attack method utilizing a feature converter. The feature converter enables an adversary to reconstruct an image from a corresponding compromised template without knowledge about the target model. The proposed method was evaluated with qualitative and quantitative measures. We achieved the Successful Attack Rate(SAR) of 0.90 on Labeled Faces in the Wild Dataset(LFW) with compromised templates of only 1280 identities.