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Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings
[article]
2021
arXiv
pre-print
Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new privacy-preserving feature representation. The core idea of our work is to drop constraints from each feature descriptor by embedding it within an affine subspace containing the
arXiv:2006.06634v3
fatcat:c5y5urkycnepjlz7x34lshmtlq