The existing research on digital image forensics at home and abroad provides certain technical support for the authenticity appraisal of digital images. However, most users have not fully examined the difference between the normal processing of digital images and malicious tampering behavior, which not only stifles the normal processing of digital images such as beautification and polishing, but also hinders the development of digital image processing technology and the promotion of corresponding software. This means that it is not only about simply detecting whether the image has been processed but also interpreting and evaluating the detected processing in conjunction with the image content. Based on the user’s needs for normal image processing, and considering the difference in the authenticity of the image content before and after processing as the standard, correctly distinguishing between normal user modification and malicious tampering, is different from existing digital image forensics technology. This article proposes a digital image authenticity evaluation model based on an encoder-decoder network, which utilizes deep learning techniques to extract relevant features of digital images and construct an authenticity evaluation model. The encoder-decoder network is used to comprehensively evaluate whether the semantic information of digital images is true and trustworthy, thus avoiding misjudgment of authenticity. Finally, the effectiveness of the proposed digital image authenticity authentication model was verified through experiments.
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