An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long...
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| Format: | Article |
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MDPI AG
2024-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/21/9754 |
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| author | Sarah Tipper Hany F. Atlam Harjinder Singh Lallie |
| author_facet | Sarah Tipper Hany F. Atlam Harjinder Singh Lallie |
| author_sort | Sarah Tipper |
| collection | DOAJ |
| description | Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting inconsistencies over time. This hybrid model enhances the ability to detect deepfakes by combining spatial and temporal analysis. However, the existing research lacks systematic evaluations that comprehensively assess their effectiveness and optimal configurations. Therefore, this paper provides a comprehensive review of video deepfake detection techniques utilising hybrid CNN-LSTM models. It systematically investigates state-of-the-art techniques, highlighting common feature extraction approaches and widely used datasets for training and testing. This paper also evaluates model performance across different datasets, identifies key factors influencing detection accuracy, and explores how CNN-LSTM models can be optimised. It also compares CNN-LSTM models with non-LSTM approaches, addresses implementation challenges, and proposes solutions for them. Lastly, open issues and future research directions of video deepfake detection using CNN-LSTM will be discussed. This paper provides valuable insights for researchers and cyber security professionals by reviewing CNN-LSTM models for video deepfake detection contributing to the advancement of robust and effective deepfake detection systems. |
| format | Article |
| id | doaj-art-7c73fd9582a149cda1d7da9e240aa323 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7c73fd9582a149cda1d7da9e240aa3232025-08-20T02:49:49ZengMDPI AGApplied Sciences2076-34172024-10-011421975410.3390/app14219754An Investigation into the Utilisation of CNN with LSTM for Video Deepfake DetectionSarah Tipper0Hany F. Atlam1Harjinder Singh Lallie2Cyber Security Centre, Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UKCyber Security Centre, Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UKCyber Security Centre, Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UKVideo deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting inconsistencies over time. This hybrid model enhances the ability to detect deepfakes by combining spatial and temporal analysis. However, the existing research lacks systematic evaluations that comprehensively assess their effectiveness and optimal configurations. Therefore, this paper provides a comprehensive review of video deepfake detection techniques utilising hybrid CNN-LSTM models. It systematically investigates state-of-the-art techniques, highlighting common feature extraction approaches and widely used datasets for training and testing. This paper also evaluates model performance across different datasets, identifies key factors influencing detection accuracy, and explores how CNN-LSTM models can be optimised. It also compares CNN-LSTM models with non-LSTM approaches, addresses implementation challenges, and proposes solutions for them. Lastly, open issues and future research directions of video deepfake detection using CNN-LSTM will be discussed. This paper provides valuable insights for researchers and cyber security professionals by reviewing CNN-LSTM models for video deepfake detection contributing to the advancement of robust and effective deepfake detection systems.https://www.mdpi.com/2076-3417/14/21/9754deepfakeconvolutional neural network (CNN)long short-term memory (LSTM)video deepfake detectionfeature extraction |
| spellingShingle | Sarah Tipper Hany F. Atlam Harjinder Singh Lallie An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection Applied Sciences deepfake convolutional neural network (CNN) long short-term memory (LSTM) video deepfake detection feature extraction |
| title | An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection |
| title_full | An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection |
| title_fullStr | An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection |
| title_full_unstemmed | An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection |
| title_short | An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection |
| title_sort | investigation into the utilisation of cnn with lstm for video deepfake detection |
| topic | deepfake convolutional neural network (CNN) long short-term memory (LSTM) video deepfake detection feature extraction |
| url | https://www.mdpi.com/2076-3417/14/21/9754 |
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