CNN-based Camera Model Identification Using Image Noise in Frequency Domain

Camera model identification has been studied extensively within digital image forensics as a deterrence to secret photography and image forgery. The feasibility of convolutional neural networks (CNNs) has been proven for an image classification algorithm. CNN-based algorithms have been proposed for camera model classification, and they focus on training with image/noise in a spatial domain. However, because the periodic characteristics of image noise are one of the essential types of information for model classification, it is more efficient to train CNN models with image noise in a frequency domain. In this paper, we propose a CNN-based approach for camera model classification that trains a CNN model with high-frequency components of images in the frequency domain. In the experiments, we evaluated the accuracy of camera model/brand classification using a Dresden image dataset. We achieved 97.35% and 99.32% accuracy, respectively, for 14-model classification of 256x256 image patches and full images. Using this approach, our results indicated a 1.84% and 1.35% improvement, respectively, compared with a state-of-the-art method. We also achieved 100% accuracy for 10-brand classification.