Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features

This article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network (CNN)-based method to accurately localize the tampered regions, which combines color filter array (CFA) features. The CFA interpolation algorithm introduces the correlation and consistency among the pixels, which can be easily destroyed by most image processing operations. The proposed CNN method can effectively distinguish the traces caused by copy-move forgeries and some post-processing operations. Additionally, it can utilize the classification result to guide the feature extraction, which can enhance the robustness of the learned features. This article, per the authors, tests the proposed method in several experiments. The results demonstrate the efficiency of the method on different forgeries and quantifies its robustness and sensitivity.

Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features

This article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network (CNN)-based method to accurately localize the tampered regions, which combines color filter array (CFA) features. The CFA interpolation algorithm introduces the correlation and consistency among the pixels, which can be easily destroyed by most image processing operations. The proposed CNN method can effectively distinguish the traces caused by copy-move forgeries and some post-processing operations. Additionally, it can utilize the classification result to guide the feature extraction, which can enhance the robustness of the learned features. This article, per the authors, tests the proposed method in several experiments. The results demonstrate the efficiency of the method on different forgeries and quantifies its robustness and sensitivity.

Blind CFA Interpolation Detection Based on the Entropy

Blind CFA interpolation detection，which identifies the demosaicing method used in digital camera by analyzing output images, provides many efficient tools for digital image forensics．In this paper, we proposes an approach of blind CFA interpolation detection based on the entropy of the correlative coefficients．By solving the pixel matrix equation, the CFA interpolation coefficients are calculated and the entropy of the coefficients are obtained, and they are further fed to SVM classifier to identify forgery. The experimental results show a high accuracy on blind CFA interpolation detection．Compared with existing ones，the proposed method in this paper indicates a better performance on the robnstness especially against lossy JPEG compression.

Research and Design of Color Restoration Algorithm for Demosaicing Bayer Pattern Images

This paper analyses the process of obtaining real color image based on a single CCD/CMOS sensor. Bayer CFA Interpolation algorithm and White Balance algorithm are presented. The proposed interpolation algorithm which can obtain better color image and is easier to implement by hardware is provided. In order to cancel chromatic aberration, one White Balance algorithm which aims at sequential image is presented and process of FPGA design is given. Experimental results show that the design works normally. The color of corrected picture is vivid compared to the real world.