With the development of technology and the increase in the need to use the internet and the transmission of personal data and save it on the cloud and personal computers and with the increase in security risks represented by penetrations and cyber threats, and among these threats are those represented by the use of deep learning techniques to produce deep fake images that are mostly used to violate cyber security through threats such as the ransom threat or publishing fake news and other cyber risks, so there was an urgent need to establish systems to detect deep forgery in images or videos, in this paper a system was proposed to detect deep forgery based on the principle of using the diffusion model based on the graph based on image segmentation where the system transforms the image to graph and then segment it into three areas which are fake, real and background. And do purification of the adversarial content in these areas using diffusion technology, and then identify the fake areas in the image and use GCN in order to distinguish between real and fake images. In order to evaluate the proposed method, it was compared with the other methods on different data sets (CELEB-DF (V2) data set, Face Forensics++ data set, and wild deep fake). The result shows that the proposed system achieved better results than the approaches in the literature.
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