• Ashwini P Bogar
• Anvitha N
• Ananya Gokuldas Prabhu
• Akhila A Pallegar

Deepfakes—hyper-realistic manipulated videos and images—have become more common thanks to deep learning, a flexible technique with applications in computer vision, machine learning, and natural language processing. Deep Fakes have many creative opportunities, but they also carry a number of serious hazards, such as the proliferation of financial scams, celebrity pornography, and fake news. It is crucial to identify and lessen the negative consequences of deep fakes, particularly for susceptible people like politicians and celebrities. With an emphasis on deep learning methods, this study provides a thorough evaluation of deepfake production and detection technologies. We analyze the shortcomings of the available databases in society and the detection techniques used today. We provide an accurate and automated deepfake detection system by using deep learning techniques instead of more conventional methods. Specifically, we use datasets to assess the efficacy of the LSTM and ResNet models for deep fake video classification

Deepfake, deep learning, ResNet, LSTM

"Design and Development of Fake Video Identification through Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f31-f36, May-2024, Available :http://www.jetir.org/papers/JETIR2405503.pdf

"Design and Development of Fake Video Identification through Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf31-f36, May-2024, Available at : http://www.jetir.org/papers/JETIR2405503.pdf