Image anonymization for PRNU forensics: A set theoretic framework addressing compression resilience
Image forensics using sensor photo-response nonuniformity (PRNU) provides a powerful method for associating an image with the camera that captured the image. To preserve privacy despite the availability of this powerful tool, we present a new framework for image anonymization. We formulate anonymization as a feasibility problem subject to multiple constraints that seek to ensure non-detectability of the PRNU fingerprint, visual fidelity to the original image, and compatibility to compression. A feasible anonymized image is then obtained via the method of projections onto convex sets using the inherent convexity of several constraints and convex approximations for the others. We demonstrate the effectiveness of our framework by benchmarking it over a publicly available dataset of images from multiple cameras and comparing against a recently presented alternative method. In the process we also highlight a key failing of several prior methods that fail to account for quantization in the compression process and suffer from catastrophic loss of anonymity when the anonymized image is stored in a compressed format, as would commonly be the case in realistic applications. We demonstrate specifically that the compression compatibility constraints we introduce help ensure that our method does not encounter this common pitfall.