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Deepfakes, AI-generated synthetic media, present a significant challenge in misinformation, security, and privacy. This chapter explores the role of recurrent neural networks (RNNs) in both generating and detecting deepfakes, particularly in video sequences. Due to their ability to process temporal dependencies, RNNs enhance deepfake realism while also serving as a tool for detecting inconsistencies. The first section examines how RNNs and generative adversarial networks (GANs) contribute to deepfake creation. By modeling temporal correlations in video frames, RNNs improve synthetic media quality, making detection more difficult. We detail the mathematical foundations of RNN-based generative models and their integration into GAN frameworks, discussing training challenges and real-world pattern replication. The second section focuses on RNN-based deepfake detection. By treating detection as a classification problem, RNNs identify temporal anomalies in video sequences using hidden state dynamics. We propose an RNN-based detection architecture that exploits these inconsistencies, evaluating its effectiveness through standard classification metrics. This chapter highlights RNNs’ dual role in advancing synthetic media while strengthening defenses against digital fraud.

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Temporal Deepfake Generation and Detection in Video Sequences Using Recurrent Neural Networks (RNNs)

  • 章节

  • 发表日期：

    2025年

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  • S. S. Iyengar,
  • Seyedsina Nabavirazavi,
  • Yashas Hariprasad,
  • Prasad HB,
  • C. Krishna Mohan

摘要

Deepfakes, AI-generated synthetic media, present a significant challenge in misinformation, security, and privacy. This chapter explores the role of recurrent neural networks (RNNs) in both generating and detecting deepfakes, particularly in video sequences. Due to their ability to process temporal dependencies, RNNs enhance deepfake realism while also serving as a tool for detecting inconsistencies. The first section examines how RNNs and generative adversarial networks (GANs) contribute to deepfake creation. By modeling temporal correlations in video frames, RNNs improve synthetic media quality, making detection more difficult. We detail the mathematical foundations of RNN-based generative models and their integration into GAN frameworks, discussing training challenges and real-world pattern replication. The second section focuses on RNN-based deepfake detection. By treating detection as a classification problem, RNNs identify temporal anomalies in video sequences using hidden state dynamics. We propose an RNN-based detection architecture that exploits these inconsistencies, evaluating its effectiveness through standard classification metrics. This chapter highlights RNNs’ dual role in advancing synthetic media while strengthening defenses against digital fraud.