Deep Learning Technique for Recognition of Deep Fake Videos

When it comes to deep face recognition, massive data analysis, speech recognition, and image recognition, deep learning approaches have proven to be remarkably effective. Deep fakes represent a fusion of deep learning techniques and deceptive practices, wherein artificial intelligence is employed to fabricate counterfeit visual content, such as photos or films. These manipulated media artifacts are commonly exploited for purposes likepolitical manipulation, the spread of disinformation, and the production of explicit material. The demand for Al technologies is substantial, giving rise to concerns pertaining to privacy, security, and ethics. This study examines the computer vision-based characteristics of digital content in order to ascertain its integrity. The proposed methodology involves the analysis of image frames to identify computer vision features through the utilization of a fuzzy clustering feature extraction technique. The identification of video and image modifications is achieved by employing paired learning techniques in conjunction with a deep belief network that incorporates loss processing. The utilization of this particular strategy has been seen to enhance the precision of detection by 98% across various datasets [1].