SOFIR: Securely outsourced Forensic image recognition

Abstract

Forensic image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal images. To detect and mitigate the distribution of illegal images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless. We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal images in the given interval, while otherwise keeping the company's legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world.