Performance Analysis of Face Forgery Recognition and Classification Using Advanced Deep Learning Methods

The adoption of web technology has come to be accompanied by a number of worrying security issues, one of which is deep fakes that are now counted among the top visual deceits in the field. The need for identifying such manipulations which is on the rise is the need for stronger methods that can be used to identify such manipulations. This article deals with the usage of fully connected neural networks (FCNN), convolutional neural networks (CNN), and deep convolutional neural networks (DCNN) to determine if a presented facial image is original or fake. In this case, the methods apply the use of the improvements in the feature extracting techniques to catch even the smallest differences in modified materials. Tests conducted on kaggle benchmark datasets depend on that that the methods are the best for it as a solution for safeguarding reliable and efficient forgery detection. The outcome is suggestive of that integration of deep learning methods like CNN, FCNN, and DCNN automated systems has the potential for advancing the struggle against manipulation in media field. As compared with the other models, the CNN is excelling in my testing and it is far better than the rest. More precisely, the CNN is the most perfect while FCNN had its drawbacks in the precision and specificity.