Trust Nobody: Privacy-Preserving Proofs for Edited Photos with Your Laptop

The Internet has plenty of images that are transformations (e.g., resize, blur) of confidential original images. Several scenarios (e.g., selling images over the Internet, fighting disinformation, detecting deep fakes) would highly benefit from systems allowing to verify that an image is the result of a transformation applied to a confidential authentic image. In this paper, we focus on systems for proving and verifying the correctness of transformations of authentic images guaranteeing: 1) confidentiality (i.e., the original image remains private), 2) efficient proof generation (i.e., the proof certifying the correctness of the transformation can be computed with a common laptop) even for high-resolution images, 3) authenticity (i.e., only the advertised transformations have been applied) and 4) fast detection of fraud proofs.. Our contribution consists of new definitions modelling confidentiality and adaptive adversaries, techniques to speed up the prover of a ZK-snark, an efficient construction relying on ad-hoc signatures and hashes, and a less efficient construction that works according to signatures and hashes included in the C2PA specifications. Experimental results confirm the viability of our approach, allowing to compute an authentic transformation of a high-resolution image on a common computer. Prior results instead either require expensive computing resources or provide unsatisfying confidentiality.