Dual Stream Computer-Generated Image Detection Network Based On Channel Joint And Softpool

• URL: http://arxiv.org/abs/2207.03205v1
• Date: Thu, 7 Jul 2022 10:19:04 GMT
• Title: Dual Stream Computer-Generated Image Detection Network Based On Channel Joint And Softpool
• Authors: Ziyi Xi, Hao Lin, Weiqi Luo
• Abstract summary: How to distinguish the computer-generated graphics (CG) from the photographs (PG) has become an important topic in the field of digital image forensics. This paper proposes a dual stream convolutional neural network based on channel joint and softpool. Experiments on SPL2018 and DsTok show that the proposed method outperforms existing methods.
• Score: 10.743766528498943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of computer graphics technology, the images synthesized by computer software become more and more closer to the photographs. While computer graphics technology brings us a grand visual feast in the field of games and movies, it may also be utilized by someone with bad intentions to guide public opinions and cause political crisis or social unrest. Therefore, how to distinguish the computer-generated graphics (CG) from the photographs (PG) has become an important topic in the field of digital image forensics. This paper proposes a dual stream convolutional neural network based on channel joint and softpool. The proposed network architecture includes a residual module for extracting image noise information and a joint channel information extraction module for capturing the shallow semantic information of image. In addition, we also design a residual structure to enhance feature extraction and reduce the loss of information in residual flow. The joint channel information extraction module can obtain the shallow semantic information of the input image which can be used as the information supplement block of the residual module. The whole network uses SoftPool to reduce the information loss of down-sampling for image. Finally, we fuse the two flows to get the classification results. Experiments on SPL2018 and DsTok show that the proposed method outperforms existing methods, especially on the DsTok dataset. For example, the performance of our model surpasses the state-of-the-art by a large margin of 3%.

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