PRNU noise extraction algorithm based on U-shaped Transformer deep network
Photo-Response Non-Uniformity(PRNU)noise can be used as the fingerprint of the camera and source camera identification of digital images because of its uniqueness and stability.In order to improve the accuracy and efficiency of source camera identification,this paper proposes a PRNU noise extraction algorithm based on U-shaped Transformer deep network(Uformer).The network uses a Transformer block based on Locally-enhanced Win-dow(LeWin),which can effectively extract local context information with low computational complexity.Secondly,the network uses a Multi-Scale Restoration Modulator in the form of multi-scale spatial deviation,which can adaptive-ly adjust the multi-layer features of the Uformer decoder,so as to better extract the potential PRNU camera fingerprints in the image.The experimental results on the Dresden dataset show that the AUC values of the proposed algorithm at 128×128 pixels,256×256 pixels and 512×512 pixels are 0.836 8,0.925 0 and 0.972 0,respectively,and the Kappa values are 0.900 5,0.744 7 and 0.473 7,respectively.They are better than the existing methods.
photo-response non-uniformitysource camera identificationTransformerdeep learningimage processing
NETL
NSTL
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