Source Camera Identification Based On Content-Adaptive Fusion Network
This addresses a hard task in forensics for small query images, but it appears incremental as it builds on existing CNN methods with modifications.
The paper tackles source camera identification for small-size images by proposing a content-adaptive fusion network, achieving satisfactory results in experiments.
Source camera identification is still a hard task in forensics community, especially for the case of the small query image size. In this paper, we propose a solution to identify the source camera of the small-size images: content-adaptive fusion network. In order to learn better feature representation from the input data, content-adaptive convolutional neural networks(CA-CNN) are constructed. We add a convolutional layer in preprocessing stage. Moreover, with the purpose of capturing more comprehensive information, we parallel three CA-CNNs: CA3-CNN, CA5-CNN, CA7-CNN to get the content-adaptive fusion network. The difference of three CA-CNNs lies in the convolutional kernel size of pre-processing layer. The experimental results show that the proposed method is practicable and satisfactory.