Deepfake Image Verification using DCNN with MobileNetV2

In this contemporary era we find so many things happening around us. They may or may not be rational, but we accept them and go on the path they show without knowing who is behind all that. This all is done by communication channels that make use of Artificial Intelligence to come up with content that looks as if some human being has done but it is a few lines of code that generate that content called Deepfake. This technology has been a boon for entertainment, fiction, art, etc. From our study, we found that there is research that found a deep fake image detection machine. This machine can adequately detect any changes made in the image. This has helped us go a bit closer to our goal by giving us creative ideas that ensure more authenticity and integrity of media. We can achieve our goal by using various fields of Deep Learning and Computer Vision. DCNNs and MobileNetV2 have strong object detection identification and classification abilities and are perfect tools to help us get rid of the deepfake menace. Since technology is rapidly changing it brings new challenges with it. So, to cope with the ongoing challenges our system would have self-learning abilities and would effectively handle any deepfake methods and strategies. This system would serve as a mother to many other advanced deepfake detection systems that safeguard our visual media. Our target is to build a flexible, easy-to-use system that can effectively work in different environments and in collaboration with other software applications. These models would dominate deepfake expansion with 99% accuracy. Thus, our work would play a great role in protecting our data and help provide a safe and secure environment for every user.