Camera model identification with residual neural network
Yunshu Chen, Yue Huang, Xinghao Ding · 2017
With the development of multimedia, camera model identification from given images has attract large attentions in cyber-forensic area recently. The task has achieved a great improvement due to some deep learning methods, where the features are extracted with the stacked architectures. However, it should be considered that both low-level and highlevel features have contributions to the recognition. In this paper, we investigate the task with another deep learning model, residual neural network (ResNet). Proposed framework has been evaluated on the experiments of brand-attribution, model-attribution and device-attribution. Besides, we also include cell phone model identification in the brand-attribution experiment for the first time. The classification results have demonstrated that the proposed work has the ability of enhancing the identification performances compared with existing methods in each specific task. The proposed work can be considered as an effective approach on image forensics.