SCCRNet: a framework for source camera identification on digital images

Identifying the source of digital images is a critical task in digital image forensics. A novel architecture is proposed using a combination of Convolutional layers and residual blocks to distinguish source cameras. The network architecture comprises convolutional layers, residual blocks, batch normalization layers, a fully connected layer and a softmax layer. Architecture aids in learning and extracting the features for identifying the model and sensor level patterns for source camera identification. Multiple patches are taken from each image to increase the sample space size. The experiments on the MICHE-I dataset show an accuracy of 99.47% for model level source camera identification and 96.03% for sensor level identification. Thus, the proposed method is more accurate than the state-of-the-art methods on the MICHE-1 dataset. The proposed architecture yields comparable results on Dresden and VISION datasets also. Moreover, a technique is also proposed to identify the images of unknown camera models by setting a threshold value for the output prediction score.