Augmented convolutional feature maps for robust CNN-based camera model identification

Identifying the model of the camera that captured an image is an important forensic problem. While several algorithms have been proposed to accomplish this, their performance degrades significantly if the image is subject to post-processing. This is problematic since social media applications and photo-sharing websites typically resize and recompress images. In this paper, we propose a new convolutional neural network based approach to performing camera model identification that is robust to resampling and recompression. To accomplish this, we propose a new approach to low-level feature extraction that uses both a constrained convolutional layer and a nonlinear residual feature extractor in parallel. The feature maps produced by both of these layers are then concatenated and passed to subsequent convolutional layers for further feature extraction. Experimental results show that our proposed approach can significantly improve camera model identification performance in resampled and recompressed images.