In this paper, we propose a camera model identification method based on deep convolutional neural networks (CNNs). Unlike traditional methods, CNNs can automatically and simultaneously extract features and learn to classify during the learning process. A layer of preprocessing is added to the CNN model, and consists of a high pass filter which is applied to the input image. Before feeding the CNN, we examined the CNN model with two types of residuals. The convolution and classification are then processed inside the network. The CNN outputs an identification score for each camera model. Experimental comparison with a classical two steps machine learning approach shows that the proposed method can achieve significant detection performance. The well known object recognition CNN models, AlexNet and GoogleNet, are also examined.
A preprocessing layer is added to the CNN architecture in the work REF . And two types of residuals in preprocessing stage are evaluated: high-pass filtering residual and the residual noise extracted by subtracting the denoised version of the image from the image itself.
Camera model identification with the use of deep convolutional neural networks
2016 IEEE International Workshop on Information Forensics and Security (WIFS)
2016 IEEE International Workshop on Information Forensics and Security (WIFS)
Computer Science