Cross-Class and Inter-class Alignment Based Camera Source Identification for Re-compression Images

Guowen Zhang, Bo Wang, Yabin Li

Abstract: With the sophisticated machine learning technology developing the state of art of model based camera source identification has achieved a high level of accuracy in the case of matching identification, which means the feature vectors of training and test sets follow the same statistical distribution. For a more practical scenario, identifying the camera source of an image transmitted via social media applications and internet is a much more interesting and challenging work. Undergoing serials of manipulations, re-compression for instance, the feature vectors of training and test sets mismatch, thus decreasing the identification accuracy. In this paper, cross-class and inter-class alignment based algorithms, inspired by transfer learning, are proposed to minimize the distribution difference between the training and the test sets. Experiments on four cameras with five image quality factors indicate that the proposed cross-class, inter-class alignment based algorithms and their combination outperform the existing LBP method, and presents high identification accuracies in re-compression images.