Abstract

The photo-response non-uniformity (PRNU) noise of imaging sensors significantly aids digital forensics and judicial identification, as it can be used as the fingerprint for uniquely identifying individual imaging devices. During the PRNU fingerprint extraction, it is very important for source camera identification to estimate the natural noise from real-world images. Most existing deep learning-based neural networks have a large number of model parameters, and they may not be practical in realistic scenarios such as deploying the model on small devices like smartphones and remote forensics equipment. In this paper, we present a lightweight PRNU fingerprint extraction algorithm based on an invertible denoising network (InvDN) for source camera identification. Specifically, to reduce the number of parameters, the deep network uses a constant amount of memory to compute the gradient and employs the same parameters for both forward and backward propagation. In addition, by introducing an information-loss-less reversible network, more complete PRNU fingerprint information can be extracted. Experimental results show that this algorithm exhibits superior PRNU fingerprint extraction performance and generalization ability compared to the state-of-the-art PRNU fingerprint extraction algorithms.