A Research Paper on AI-based DeepFake Detection for Human Face Images and Videos

This research is about finding a way to detect deepfake images using advanced computer techniques. The system we made combines a neural network called Xception with good ways to prepare the images and a website that is easy to use.   The system can find images by looking at the faces and finding small things that are not right. Using something called transfer learning makes the system work better and faster. Finding faces in the images helps make it more accurate by focusing on the parts.   One of the things about this work is that we made a solution that is practical and easy to use. We put the system on a website so people who are not experts can use it to find deepfakes in time. This makes it useful for things like checking media looking at digital evidence and making sure online content is real. The results of our tests show that the system is very good at finding deepfakes and it works well in situations. The system is also reliable. Can find fake content with high accuracy.   However we also found some challenges. It is still hard to find deepfakes that're very good especially if the image quality is not good. We can also make the system better by looking at videos and adding images to the training set.   In the future we can work on making the system better so it can handle new ways of making deepfakes. Using different ways to detect deepfakes like looking at frequencies and sequences can make it work even better. We can also put the system on devices and integrate it with social media to make it more useful.   In the end this research helps us find deepfakes by giving us a scalable and practical solution. As digital media keeps changing systems like this will be very important for keeping content trustworthy and authentic. Deepfake detection is a part of this and our system is a step in the right direction, for detecting deepfakes.

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