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Many computer vision systems require users to upload image features to thecloud for processing and storage. These features can be exploited to recoversensitive information about the scene or subjects, e.g., by reconstructing theappearance of the original image. To address this privacy concern, we propose anew privacy-preserving feature representation. The core idea of our work is todrop constraints from each feature descriptor by embedding it within an affinesubspace containing the original feature as well as adversarial featuresamples. Feature matching on the privacy-preserving representation is enabledbased on the notion of subspace-to-subspace distance. We experimentallydemonstrate the effectiveness of our method and its high practical relevancefor the applications of visual localization and mapping as well as faceauthentication. Compared to the original features, our approach makes itsignificantly more difficult for an adversary to recover private information.

Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings