Source Camera Identification Based on Content-Adaptive Fusion Residual Networks
P. Yang, R. Ni, Yao Zhao, Wei Zhao
Source camera identification is still a hard task in forensics community, especially for the case of the query images with small size. In this paper, we propose a solution to identify the source camera of the small-size images: content-adaptive fusion residual networks. According to the differences of the image contents, firstly, the images are divided into three subsets: saturation, smoothness and others. Then, we train three fusion residual networks for saturated images, smooth images, and others, separately, through transform learning. The fusion residual networks is formed with three paralleled residual networks and the difference of three residual networks lies in the convolutional kernel size of preprocessing layer. The features learned from the last residual blocks of three residual networks are fused and fed into softmax classifier. In particular, the residual networks is designed to learn better feature representation from the input data. The convolutional operation is added in preprocessing stage and three residual blocks are used. The experiment results show that the proposed method has satisfactory performances at three levels of source camera identification: brand level, model level, and device level.