An Enhanced Deepfake Video Detection Technique

A deepfake is a piece of digital material that has been altered, such as a picture or a video in which the subject’s likeness has been substituted. Deepfake is a real danger to society since it distorts the opinions and perceptions of those around us. Deep learning, which is referred to as “a subset of AI,” is used to create deepfake. Deep learning is a combination of algorithms with the ability to learn and make sensible decisions on their own. People may be led astray by deepfake into believing something to be genuine when it is not. In the hands of internet scammers and cybercriminals, such cutting-edge technologies as deepfake can become dangerous tools. Deepfakes can be hard to spot, making it tough to stop the dissemination of fake content. Additionally, anyone with little programming knowledge can make deepfakes because they can be made with relatively simple tools. Understanding the dangers of deepfake, we are motivated to understand the technology behind deepfake generation, search for technologies to counter this threat, and develop effective detection techniques to accurately identify deepfake videos with high efficiency. In order to achieve this, we have conducted an extensive literature search on various algorithms for creating and detecting deepfake videos. We have identified their relative strengths and limitations, and areas for potential improvements. We have developed to enhance the current detection techniques by designing and developing a new technique that can effectively identify fake videos with multiple faces.