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.
“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)
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Hybrid Model for Deepfake Detection
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Deepfake Image Classification Using Decision (Binary) Tree Deep Learning
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Beyond Frames: 3D-CoAtNet for Generalizable Deepfake Video Detection
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Mining the online infosphere: A survey
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Context-Aware Deepfake Detection for Securing AI-Driven Financial Transactions
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Comparative Evaluation of Xception and DenseNet121 for Robust Deepfake Detection
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Temporal Deepfake Detection using CNN with Spatio-Temporal Features
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Deep Fake Generation and Detection
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Robust Deepfake Detection System with Deep Learning Techniques
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- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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