Deepfake Detection Using Deep Learning

We came up with a way to detect deepfakes using deep learning. Our method combines Convolutional Neural Networks (CNNs) with some useful image processing steps. This system first pulls out important details from faces. It does this by finding the faces, making them uniform (normalizing), and adjusting their size. Then, a specially trained deep learning model checks what they are. Using transfer learning also makes the model work even better and cuts down on how much effort it takes to train it. Our tests showed that this system is really good at telling the difference between real and fake media, and it worked well on different sets of data. We used measurements like accuracy, precision, recall, and F1-score, and these all confirm that the model is dependable and does what it's supposed to. Plus, we used tools like heatmaps to see how the model makes its decisions. This helps us understand it better and trust it more. But our study also pointed out some issues. For example, it didn't work as well on images that were heavily compressed, and it struggled to catch some of the more advanced deepfake methods. Even with these shortcomings, though, our method turned out to be a strong and effective way to find deepfakes. Since fake media is becoming more and more common, we really need good detection systems to keep digital content authentic and trustworthy. Our method helps with this goal, and it lays the groundwork for more research to make detection even more accurate and useful in everyday situations.

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