Optimal counterforensics for histogram-based forensics

Abstract

There has been a recent interest in counterforensics as an adversarial approach to forensic detectors. Most of the existing counterforensics strategies, although successful, are based on heuristic criteria, and their optimality is not proven. In this paper the optimal modification strategy of a content in order to fool a histogram-based forensics detector is derived. The proposed attack relies on the assumption of a convex cost function; special attention is paid to the Euclidean norm, obtaining the optimal attack in the MSE sense. In order to prove the usefulness of the proposed strategy, we employ it to successfully attack a well-known algorithm for detecting double JPEG compression.