Tampering detection and localization base on sample guidance and individual camera device convolutional neural network features

Abstract

AbstractThis paper proposes an image tampering detection algorithm based on sample guidance and individual camera device's convolutional neural network (CNN) features (SGICD‐CF) to address the challenges in the authenticity and integrity of images. Due to the development of the digital image processing technology, which makes image editing and processing, image tampering and forgery easy and lot simplified, thus solving the problem of image tamper detection, to maintain information security. The principle of SGICD‐CF assumes that pixels of the pristine image come from a single camera device, but on the contrary, if an image to be tested is spliced by multiple images from different cameras, then the pixels from the multiple camera devices will be detected. SGICD‐CF divides the image to be tested into 64 × 64 pixel image patches, extracts the camera‐related features and some camera model‐related information of image patches by source camera identification network (SCI‐Net) which is proposed by us, and obtains the classification confidence degree of the image patch. Furthermore, it determines whether the image patch contains foreign pixels according to the obtained confidence degree and finally determines whether the image was tampered according to the classification results of all the image patches, thus locating the tampered area. However, the experimental results show that SGICD‐CF can detect and locate the tampered area of an image accurately and our methods have a better performance than other existing methods. Our algorithm can achieve an average correct rate of 0.855 on the synthetic data set based on Dresden, which is higher than other existing detection methods.