A fuzzy fusion approach for modified contrast enhancement based image forensics against attacks

In today’s digital age the trustworthy towards image is distorting because of malicious forgery images. The issues related to the multimedia security have led to the research focus towards tampering detection. The main objective of the work is to develop robust and forensic detection framework against post processing. It is also essential to enhance the security against attacks. In this paper, a Modified Contrast Enhancement based Forensics (MCEF) method based on Fuzzy Fusion is proposed against post-processing activity. First, we check for the histogram peaks and gaps as a result of contrast enhancement which is used in the latest technique. From the standpoint of attackers, we use two types of attacks, CE trace hiding attack and CE trace forging attack, which could invalidate the forensic detector and fabricate two types of forensic errors, consequently. The CE trace hiding attack is implemented by integrating local random dithering into the form of pixel value mapping. The CE trace forging attack is proposed by modifying the grey level histogram of a target pixel region to fraudulent peak/gap artifacts. Then both attacks are added to enhanced images as a post processing activity. As a result the gaps get disappeared, but introduced sudden peaks. Then, feature selection methods in conjunction with fuzzy fusion approach is suggested to enhance the robustness of tamper detection methods. The threshold value for contrast detection is increased, so we can identify the contrast enhancement. The Artificial Neural Network (ANN) is used instead of SVM, it increases the robustness and accuracy of the digital images. The proposed methodology will be implemented using MATLAB and validated by comparing with the conventional techniques.