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.
In our previous work @cite_9 , we proposed the LCNN to detect recaptured images. The LCNN has better detection performance than the algorithms based on handcrafted features. The architecture of the LCNN is shown in Fig 1. There has a preprocessing layer, five convolutional layers and a softmax layer. Laplician filtering operation is put into preprocessing layer. The convolutional layer contains four operations: convolution, Batch-Normalization, ReLu, and average pooling. The numbers of feature maps in five convolutional layers are 8, 16, 32, 64, and 128, respectively. In order to avoid overfitting, we applied global average pooling to the last pooling layer and directly fed the output of global average pooling into softmax layer. What's more, the Batch-Normalization layer is used. It has been proved that it is an effective mode to accelerate convergence. Owing to the generalization of convolutional nerual networks, we also make use of the LCNN architecture in this work.
@cite_9
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Recapture image forensics has drawn much attention in public security forensics. Although some algorithms have been proposed to deal with it, there is still great challenge for small-size images. In this paper, we propose a generalized model for small-size recapture image forensics based on Laplacian Convolutional Neural Networks. Different from other Convolutional Neural Networks models, We put signal enhancement layer into Convolutional Neural Networks structure and Laplacian filter is used in the signal enhancement layer. We test the proposed method on four kinds of small-size image databases. The experimental results have demonstrate that the proposed algorithm is effective. The detection accuracies for different image size database are all above 95 .