Deepfake detection using genconvit

Deepfake detection can be defined as the detection of the fake images and videos generated by other deep learning methods. And other deep learning methods are used to generate Deepfakes. Ultimately, deepfake detection serves to detect and reveal the manipulations take place from authentic images and videos. In this project, we use GenconVIT which consists of 3 phases, Pre-GD, GD, Post-GD. Each phase has a different function. The first phase or called initial phase is called Pre-Generative Design. The second phase is called Generative Design (GD), and it is the main phase where the computer algorithms play a key process and this phase has three sections called Generate, Evaluate, Evolve. The final phase is called Post-Generative Design; it is called finalizing phase. The main goal of this project is real and fake image detection where it will compare both real and fake images and provide the differences from those real and fake images. It doesn't just determine if the image is real or fake because applying some algorithms will determine this. And the are some performance measures like Precision, Recall and F-1 Score. Let us start with Precision, this will calculate the Precision of your Positive predictions and for Recall your measuring the ability to find all the Positive instances. And F-1 score will measure the value of Precision and Recall evenly.