A Novel Decision Fusion Approaches for Image Forensics Analysis

Abstract

A Forensic Image is a  is often accompanied by a calculated Hash signature to validate that the image is an exact duplicate of the original. It is mainly focus on detection of artifacts introduced by single processing tool. Hence making it necessary for developing several for detection of artifacts. In this paper we introduce two theoretical frameworks, based on Dempster-Shafer's Theory of Evidence and on Fuzzy Theory respectively, to perform the fusion of heterogeneous, incomplete or conflicting outputs of forensic algorithms. Both models are easily expandable to an arbitrary number of tools, do not require tools output to be probabilistic and take into account available information about tools reliability. To validate the proposed approaches, we carried out some experiments addressing a simple yet realistic scenario in which three forensic tools exploit different artifacts introduced by double JPEG compression to detect cut and paste tampering within a specified region of an image. The results we obtained are encouraging, especially when compared with the performance of a simple decision method based on the binary OR operator.