  Most of researches on image forensics have been mainly focused on 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 are 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 poweful tools to deal with various tempering
scenarios. Fusion of forgery detection tools based on Fuzzy Inference System
has been used before for addressing this problem. Adjusting the membership
functions and defining proper fuzzy rules for attaining to better results are
time-consuming processes. This can be accounted as main disadvantage of fuzzy
inference systems. In this paper, a Neuro-Fuzzy inference system for fusion of
forgery detection tools is developed. The neural network characteristic of
these systems provides appropriate tool for automatically adjusting the
membership functions. Moreover, 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 adaptive Neuro-Fuzzy inference system. The
outcome of the proposed method reveals that applying Neuro Fuzzy inference
systems could be a better approach for fusion of forgery detection tools.
