Colorized Image Forgery Detection based on Similarity Measurement of Gaussian Mixture Distribution

Abstract

In the era of rapid development of multi-media information, forgery detection has become an important research field of digital image security. This paper proposes a new method to detect the forged image generated by deep learning. First, the feature matrix is constructed through extracting each pixel value of channels a and b in Lab color space for the real and the forged image training set, respectively, which is used to fit the Gaussian Mixture Model (GMM) distribution. Then, the Expectation Maximization (EM) adaptation algorithm is used to re-fit the GMM for test image using the obtained GMM parameter as prior information. Finally, the similarity between two GMM is calculated for forgery detection. Experiments show that the proposed method is more accurate than the traditional SVM for forgery detection.