DeepFake Detection Using Computer Vision

Abstract

The emergence of social media has greatly accelerated the dissemination of manipulated images, misleading memes, and it is rather challenging to trace the real and deceptive material in visuals. The present study is aimed at identifying deepfake images and fake memes with the help of computer vision methods. The suggested system is based on a lightweight Convolutional Neural Network (CNN) which examines various features, such as RGB color patterns, depth data, and facial features, such as eyes, nose, and lips. This is in contrast to the traditional methods that usually use RGB features alone to detect an object because this method incorporates visual appearance and spatial depth information to enhance the level of detection. The model is trained with a big amount of real and fake images and uses preprocessing and data augmentation methods to improve the performance. The depth map estimation is capable of detecting unnatural structural changes whereas the RGB analysis is able to detect the inconsistencies in texture and color anomalies. The method suggested is effective in detecting manipulated images and memes even in complicated situations in which there is compression and editing. Moreover, the model is lightweight hence low cost in computation implying that it can be applied in real time or systems with limited resources. The proposed study will help to fight against misinformation on social media by offering a stable and scalable solution to the problem of deepfakes and fake memes.

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