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Abstract: A.I. has grown to epidemic proportions over the last years as its applied in almost all sectors to allocate workload from humans but end up being done effectively with no human intervention. A branch of A.I. called deep learning, which operates by mimicking human judgment and action through neural network systems. Nonetheless, with the increase height of the two platforms have been experienced sufficient cases of misguided individuals using tools to recycle videos, audios, and texts to achieve their agendas. This insinuates a due assumption that Generative Adversarial Networks, GANs, are central to the development of believable deepfakes. GANs have developed a crucial ability to generate videos that replace frames with material from another video source to create deepfakes videos. While GANs serves various purposes such as entertainment, teaching, and experimentation, malicious actors can misuse these deep learning techniques to manipulate videos, impacting the privacy of individuals in society. This paper conducts an analysis of different deepfake detection models, comparing their efficacy and discussing potential future extensions of deepfake technology. The study presents a novel deepfake detection approach utilizing a combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This method utilizes ResNext50 for extracting features at the frame level, while employing LSTM (Long Short-Term Memory) for video classification based on these extracted features. Various datasets are incorporated, including the deepfake detection challenge dataset (DFDC) and Face Forensics deepfake collections (FF++), combining them to achieve a high-accuracy model capable of accurately discerning between real and deepfake videos. The results of this study make a valuable contribution to the continuous endeavors aimed at improving deepfake detection abilities and ensuring privacy protection in a time heavily influenced by artificial intelligence
Aman Kumar Emaley (Tue,) studied this question.