Privacy-preserving architecture for forensic image recognition

Abstract

Forensic image recognition is an important tool in many areas of law enforcement where an agency wants to prosecute possessors of illegal images. The recognition of illegal images that might have undergone human imperceptible changes (e.g., a JPEG-recompression) is commonly done by computing a perceptual image hash function of a given image and then matching this hash with perceptual hash values in a database of previously collected illegal images. To prevent privacy violation, agencies should only learn about images that have been reliably detected as illegal and nothing else. In this work, we argue that the prevalent presence of separate departments in such agencies can be used to enforce the need-to-know principle by separating duties among them. This enables us to construct the first practically efficient architecture to perform forensic image recognition in a privacy-preserving manner. By deriving unique cryptographic keys directly from the images, we can encrypt all sensitive data and ensure that only illegal images can be recovered by the law enforcement agency while all other information remains protected.