1. Government’s Approach

The Government has implemented different approaches that can help solve the problem of deepfakes in the country. This study focuses on demonstrating the Government’s approaches to curb the deepfakes, which includes; Technological solution, Legislative solutions, Platform policies, and Media literacy solutions. The Government has reached out to implement some approaches to help deal with this problem of deepfakes and should enforce laws governing social media use, and people get educated on the possible effects and issues of deepfakes.

2. Media Literacy

To deal with the problems caused by deepfakes, people need to know more about how to use the media. We need tools to help us intelligently interact with the media, figure out what is going on, take responsibility, and not add to the problem by spreading unreliable information. Media literacy will also make people more aware of confirmation bias, filter bubbles, and echo chambers and help them understand them better. Understanding how the medium of communication works and how people share, process, and use information can help us become more active citizens who think critically.

3. Generative Adversarial Networks

We evaluate the GAN discriminator’s potential to be used as a Deepfake Detector. We explore the impact of data quality and training methods on the GAN discriminator and analyze the drawbacks and limitations of using the discriminator as a Deepfake Detector. The study of the GAN discriminator’s potential to be used as a Deep Fake detector. It looks at how data quality and training methods affect the GAN discriminator and the merits and problems of implementing it as a Deepfake detector.

4. Creating The Ultimate Deepfake Detection Architecture

With the use of a capsule network and using deep convolutional neural networks, detecting various kinds of fake media, even those that have been generated elsewhere, detecting spoofs have become easier. The improvement in network architectures and the use of training data has made the creation of forged media much simpler. A capsule network can be used to detect forged images and videos in a wide range of scenarios, including those such as replay attacks, attacks repeated at a domain, and computer-generated media.