Abstract
Deepfake technology has completely changed the production of synthetic media by allowing the alteration of photos, movies, and audio recordings. It is driven by machine learning and artificial intelligence algorithms. Deepfake provide a lot of amusement and artistic freedom, but they also pose serious problems, especially when it comes to disinformation and digital manipulation. This paper offers a thorough introduction to deepfake technology, covering all of its types, including voice synthesis, gesture control, and face-swapping. The research article delves into the fundamental workings of deepfake generation, emphasizing the part played by convolutional neural networks and generative adversarial networks in producing lifelike artificial content. The paper also investigates the methods and strategies used in detection, with a focus on the latest developments in deep neural network architectures, attention-based models, and hybrid approaches. This review article also focuses on availability of standard datasets and performance parameters for the evaluation of research models. With the aim to provide contribution to help researchers to create reliable and efficient deepfake detection systems that can stop the distribution of manipulated media and ensure the accuracy of digital content by tackling various issues, paper also focuses on key challenges and future work.
Published Version
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