Deepfake Detection System Using Hybrid CNN–VIT Architecture

The increasing use of deepfake media is a serious threat because highly realistic fake images and videos can be generated and posted online, leading to scams, identity theft, and misinformation. Because of how realistic these images and videos are, it is becoming increasingly difficult to distinguish them manually. Our project provides a softwarse application that can help determine whether an uploaded image or video is real or fake. It checks facial characteristics and the overall facial structure to identify characteristic differences between real and fake media. Additionally, it performs frequency analysis to identify characteristic artefacts that are usually created when digital processing of images occurs. To ensure that the results are interpretable, the application provides visual feedback about the facial areas that contributed to the determination, providing users with a clear understanding of why a particular image or video is real or fake. The project aims to develop a useful and easy to-use application that can be used for cybersecurity, digital forensics, and online media verification

Detection, Architecture, Hybrid

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