The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Deepfake Detection in Media Files - Audios, Images and Videos
Abstract
Recent advancement in deep learning has applied to solve various complex problems ranging from big data analytic to computer vision and human-level control. One among them is the deepfake technology which becomes a real threat to privacy, democracy, and national security. Deepfake is hyper-realistic digitally manipulated videos to depict people saying and doing things that never actually happened. This technology has been used in many fields in film industries for recreating videos without re-shooting, awareness video generation such as creating voices of those who have lost theirs or updating episodes of movies without re-shooting them at very low cost. This technology has many harmful usages in social media, pornographic sites, etc. to deface peoples which largely dominate the positive side of this application of deep learning. Also, the creation and spreading of these videos are increasing rapidly along all fields of media files. Therefore, it is very much important to develop efficient tools that can automatically detect the deepfake in these videos and thus reduce the public harm caused by such videos. In the early stages of deepfake detection, traditional technologies like signal processing, image processing, lip-syncing, etc were used but this provides very little accuracy when combined with the recent technologies of deep learning. So, here a system is proposed that can automatically detect the deepfake in media files such as images, videos, and audios. This uses an i…
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“The model's accuracy was tested against different datasets, including the deepfake-TIMIT dataset, Face Forensic ++ , and others, and the accuracy was nearly 0.9 for all three model files.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as Nasar et al. (2020)
Citations
Cites
Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682602 · Cited by 951
Deep fake image detection based on pairwise learning
Hsu, C.-C., Zhuang, Y.-X., & Lee, C.-Y. (2020) · Applied Sciences · doi:10.3390/app10010370 · Cited by 304
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Cited by
Deepfake detection using deep learning methods: A systematic and comprehensive review
Heidari, A., Jafari Navimipour, N., Dag, H., & Unal, M. (2024) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1520 · Cited by 349
“In addition, Nasar et al. (2020) created a toolkit that uses cutting-edge technology to detect deep fakes in films, pictures, and audio.”
BackgroundInfluentialDeepfake generation and detection: Case study and challenges
Patel, Y., Tanwar, S., Gupta, R., Bhattacharya, P., Davidson, I. E., Nyameko, R., Aluvala, S., & Vimal, V. (2023) · IEEE Access · doi:10.1109/access.2023.3342107 · Cited by 186
Metadata
- Authors
- Nasar, B. F., T, S., & Lason, E. R.
- Affiliations
- Software Training and Development Centre (IN)
- Venue
- 2020 IEEE Recent Advances in Intelligent Computational Systems (RAICS)
- Publisher
- IEEE
- Published
- 2020-12-03
- Pages
- 74-79
- OpenAlex
- W3127768164
- Cited by
- 31
- References
- 2
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 10 · 2023: 11 · 2022: 1
- FWCI
- 0.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Recent Advances in Intelligent Computational Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- deepfake detection, deepfakes, manipulated video, sound manipulation, image editing, multimedia forensics, fake media, privacy risks
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- CoChat. (2026). Citation check: Deepfake Detection in Media Files - Audios, Images and Videos. https://app.cochat.ai/papers/10.1109/raics51191.2020.9332516
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- Nasar, B. F., T, S., & Lason, E. R. (2020). Deepfake Detection in Media Files - Audios, Images and Videos. 2020 IEEE Recent Advances in Intelligent Computational Systems (RAICS). https://doi.org/10.1109/raics51191.2020.9332516
