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Most of researches on image forensics have been mainly focused on detectionof artifacts introduced by a single processing tool. They lead in thedevelopment of many specialized algorithms looking for one or more particularfootprints under specific settings. Naturally, the performance of suchalgorithms are not perfect, and accordingly the provided output might be noisy,inaccurate and only partially correct. Furthermore, a forged image in practicalscenarios is often the result of utilizing several tools available byimage-processing software systems. Therefore, reliable tamper detectionrequires developing more poweful tools to deal with various temperingscenarios. Fusion of forgery detection tools based on Fuzzy Inference Systemhas been used before for addressing this problem. Adjusting the membershipfunctions and defining proper fuzzy rules for attaining to better results aretime-consuming processes. This can be accounted as main disadvantage of fuzzyinference systems. In this paper, a Neuro-Fuzzy inference system for fusion offorgery detection tools is developed. The neural network characteristic ofthese systems provides appropriate tool for automatically adjusting themembership functions. Moreover, initial fuzzy inference system is generatedbased on fuzzy clustering techniques. The proposed framework is implemented andvalidated on a benchmark image splicing data set in which three forgerydetection tools are fused based on adaptive Neuro-Fuzzy inference system. Theoutcome of the proposed method reveals that applying Neuro Fuzzy inferencesystems could be a better approach for fusion of forgery detection tools.

Feature base fusion for splicing forgery detection based on neuro fuzzy