With the maturation of computer graphics(CG)technology in the field of image generation,the realism of created images has been improved significantly.Although these technologies are widely used in daily life and bring many conveniences,they also come with many security risks.If forged images generated using CG technol-ogy are maliciously used and widely spread on the Internet and social media,they may harm the rights of individu-als and enterprises.Therefore,an innovative cross-stream attention enhanced central difference convolutional net-work was proposed,aiming at improving the accuracy of CG image detection.A dual-stream structure was con-structed in the model,in order to extract semantic features and non-semantic residual texture features from the im-age.Vanilla convolutional layers in each stream were replaced by central difference convolutions,which allowed the model to simultaneously extract pixel intensity information and pixel gradient information from the image.Fur-thermore,by introducing a cross-stream attention enhancement module,the model enhanced feature extraction ca-pability at the global level and promoted complementarity between the two feature streams.Experimental results demonstrate that this method outperforms existing methods.Additionally,a series of ablation experiments further verify the rationality of the proposed model design.
万方数据
维普
