Abstract: Photo-response non-uniformity (PRNU) referred as device fingerprint can be used for digital imaging device source identification. In view of the complexity of PRNU and a large number of unknown noises in the real image noise, this paper proposed a PRNU extraction algorithm combined with deep iterative down-up convolutional neural network (CNN) model. In particular, in order to extract the complete PSNR fingerprint from the real image noise, the resolution of the feature map was decreased and increased repetitively to efficiently employ graphics processing unit (GPU) memory and yield large receptive fields. Then, the noise residual was computed by multiple images from the same digital imaging device, which was utilized to estimate the PRNU fingerprint. The proposed algorithm was evaluated by Dresden camera database and Daxing smartphone database, and the experimental results show that the proposed algorithm achieves better recognition accuracy and universality compared with traditional methods.