TrustCLIP: Learning Private Visual Features via Adversarial Reconstruction
Vision and vision-language models rely on high-level visual representations that are increasingly used across recognition, retrieval, and multimodal reasoning pipelines. However, recent advances in generative modeling have shown that such features can often be inverted, enabling realistic reconstructions of the underlying image and raising significant privacy risks. We revisit this problem through the lens of reconstruction and propose TrustCLIP, a reconstruction-driven framework that treats a feature-conditioned generator as an explicit privacy adversary. TrustCLIP learns a projection between encoder features and downstream modules that is explicitly optimized to degrade the reconstructi...
| Authors | Nikos Athanasiou, Ilya A. Petrov, Angela Yao et al. |
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| Primary category | cs.CV |
| Published | 5 July 2026 |
| Last updated at source | 17 August 2026 |
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