Feature Base Fusion for Splicing Forgery Detection Based on Neuro Fuzzy
About The Publication
Most of the researches on image forensics has been mainly focused on the detection of artifacts introduced by a single processing tool. They lead in the development of many specialized algorithms looking for one or more particular footprints under specific settings. Naturally, the performance of such algorithms is not perfect, and accordingly, the provided output might be noisy, inaccurate and only partially correct. Furthermore, a forged image in practical scenarios is often the result of utilizing several tools available by image-processing software systems. Therefore, reliable tamper detection requires developing more powerful tools to deal with various tempering scenarios. The fusion of forgery detection tools based on the Fuzzy Inference System has been used before for addressing this problem. Adjusting the membership functions and defining proper fuzzy rules for attaining better results are time-consuming processes. This can be accounted for as the main disadvantage of fuzzy inference systems. In this paper, a Neuro-Fuzzy inference system for the fusion of forgery detection tools is developed. The neural network characteristic of these systems provides an appropriate tool for automatically adjusting the membership functions. Moreover, the initial fuzzy inference system is generated based on fuzzy clustering techniques. The proposed framework is implemented and validated on a benchmark image splicing data set in which three forgery detection tools are fused based on the adaptive Neuro-Fuzzy inference system. The outcome of the proposed method reveals that applying Neuro-Fuzzy inference systems could be a better approach to the fusion of forgery detection tools.