The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection
Abstract
Recent advances in technology have made the deep learning (DL) models available for use in a wide variety of novel applications; for example, generative adversarial network (GAN) models are capable of producing hyper-realistic images, speech, and even videos, such as the so-called “Deepfake” produced by GANs with manipulated audio and/or video clips, which are so realistic as to be indistinguishable from the real ones in human perception. Aside from innovative and legitimate applications, there are numerous nefarious or unlawful ways to use such counterfeit contents in propaganda, political campaigns, cybercrimes, extortion, etc. To meet the challenges posed by Deepfake multimedia, we propose a deep ensemble learning technique called DeepfakeStack for detecting such manipulated videos. The proposed technique combines a series of DL based state-of-art classification models and creates an improved composite classifier. Based on our experiments, it is shown that DeepfakeStack outperforms other classifiers by achieving an accuracy of 99.65% and AUROC of 1.0 score in detecting Deepfake. Therefore, our method provides a solid basis for building a Realtime Deepfake detector.
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“The results demonstrate that DeepfakeStack outperforms other accuracy, precision, recall, and F1-score techniques.”
From Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media (2024) · cites this paper as Rana & Sung (2020)
Citations
Cites
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Self-citation
Flagged first, then by citations
Research works printed but not linked: 10
- do, 2018, DeepFakes: Forensics Face Detection from GANs Using Convolutional Neural Network, International Symposium on Information Technology Convergence (ISITC 2018)
- patrini, 2019, The state of Deepfakes: reality under attack, Annual Report v 2 3
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Cited by
Sorted by citations
Deepfake Detection: A Systematic Literature Review
Rana, M. S., Nobi, M. N., Murali, B., & Sung, A. H. (2022) · IEEE Access · doi:10.1109/access.2022.3154404 · Cited by 573
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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
“Such alteration could assist various industries, including the entertainment industry (Rana & Sung, 2020).”
BackgroundA Novel Deep Learning Approach for Deepfake Image Detection
Raza, A., Munir, K., & Almutairi, M. S. (2022) · Applied Sciences · doi:10.3390/app12199820 · Cited by 222
Deepfake generation and detection, a survey
Zhang, T. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11733-y · Cited by 176
Metadata
- Authors
- Rana, M. S., & Sung, A. H.
- Affiliations
- University of Southern Mississippi (US)
- Venue
- 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom)
- Publisher
- IEEE
- Published
- 2020-08
- Pages
- 70-75
- OpenAlex
- W3080632971
- Cited by
- 133
- References
- 17
- Citations by year
- 2026: 17 · 2025: 36 · 2024: 24 · 2023: 22 · 2022: 24 · 2021: 8 · 2020: 2
- FWCI
- 4.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Cyber Security and Cloud Computing / IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud/EdgeCom) (publishedVersion)
- Aquila Digital Community (University of Southern Mississippi) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, ensemble deep learning, generative adversarial networks, manipulated video, sound manipulation, misinformation, real-time detection
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