Konferenzbeitrag
Model-Free Template Reconstruction Attack with Feature Converter
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Datum
2022
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Gesellschaft für Informatik e.V.
Zusammenfassung
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.