One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework
Open accessAbstract
Deep learning-based video manipulation methods have become widely accessible to the masses. With little to no effort, people can quickly learn how to generate deepfake (DF) videos. While deep learning-based detection methods have been proposed to identify specific types of DFs, their performance suffers for other types of deepfake methods, including real-world deepfakes, on which they are not sufficiently trained. In other words, most of the proposed deep learning-based detection methods lack transferability and generalizability. Beyond detecting a single type of DF from benchmark deepfake datasets, we focus on developing a generalized approach to detect multiple types of DFs, including deepfakes from unknown generation methods such as DeepFake-in-the-Wild (DFW) videos. To better cope with unknown and unseen deepfakes, we introduce a Convolutional LSTM-based Residual Network (CLRNet), which adopts a unique model training strategy and explores spatial as well as the temporal information in a deepfakes. Through extensive experiments, we show that existing defense methods are not ready for real-world deployment. Whereas our defense method (CLRNet) achieves far better generalization when detecting various benchmark deepfake methods (97.57% on average). Furthermore, we evaluate our approach with a high-quality DeepFake-in-the-Wild dataset, collected from the Internet containing numerous videos and having more than 150,000 frames. Our CLRNet model demonstrated that it generalizes well against high-quality DFW videos by achieving 93.86% detection accuracy, outperforming existing state-of-the-art defense methods by a considerable margin.
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It looks like me, but it isn’t me: On the societal implications of deepfakes
Fehring, J. A., & Bonaci, T. (2023) · IEEE Potentials · doi:10.1109/mpot.2022.3229823 · Cited by 9 · see citing works
A comprehensive analysis of digital video forensics techniques and challenges
Sandhya, & Kashyap, A. (2023) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00165-6 · Cited by 11 · see citing works
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability
Tariq, S., Le, B. M., & Woo, S. S. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557073 · Cited by 9 · see citing works
Self-citationZoom-DF: A Dataset for Video Conferencing Deepfake
Park, G.-W., Park, E.-J., & Woo, S. S. (2022) · doi:10.1145/3494109.3527195 · Cited by 8 · see citing works
Self-citationKim, J., Tariq, S., & Woo, S. S. (2022) · ACM Symposium on Applied Computing · doi:10.1145/3477314.3507036 · Cited by 8 · see citing works
Self-citationRevisiting face forgery detection towards generalization
Peng, C., Chen, T., Liu, D., Guo, H., Wang, N., & Gao, X. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107310 · Cited by 7 · see citing works
MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer
Tran, V.-N., Le, H.-S., Choi, P., Lee, S., & Kwon, K. (2025) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2025.3568044 · Cited by 7 · see citing works
Self-citationA Face Pre-Processing Approach to Evade Deepfake Detector
Kim, T., Kim, J., Kim, J., & Woo, S. S. (2022) · doi:10.1145/3494109.3527190 · Cited by 7 · see citing works
Self-citationAttention-based Multimodal learning framework for Generalized Audio-Visual Deepfake Detection
Masood, M., Javed, A., & Irtaza, A. (2023) · Preprint (Research Square) · doi:10.21203/rs.3.rs-3415144/v1 · Cited by 6 · see citing works
Robust cross-image adversarial watermark with JPEG resistance for defending against Deepfake models
Lin, Z., Lin, H., Lin, L., Chen, S., & Liu, X. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104459 · Cited by 6 · see citing works
Interpol review of forensic video analysis, 2019–2022
Geradts, Z., & Riphagen, Q. (2023) · Forensic Science International Synergy · doi:10.1016/j.fsisyn.2022.100309 · Cited by 6 · see citing works
A Bibliometric Analysis of Deepfakes : Trends, Applications and Challenges
Garg, D., & Gill, R. (2024) · ICST Transactions on Scalable Information Systems · doi:10.4108/eetsis.4883 · Cited by 6 · see citing works
Beyond the Screen: Evaluating Deepfake Detectors under Moiré Pattern Effects
Tariq, R., Heo, M., Woo, S. S., & Tariq, S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00446 · Cited by 7 · see citing works
Self-citationLEAT: Towards robust deepfake disruption in real-world scenarios via Latent Ensemble Attack
Shim, J., & Yoon, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127417 · Cited by 7 · see citing works
Exploiting Inconsistencies in Object Representations for Deepfake Video Detection
Bhaumik, K. K., & Woo, S. S. (2023) · doi:10.1145/3595353.3595885 · Cited by 5 · see citing works
Self-citationThe Framework of Cross-Domain and Model Adversarial Attack against Deepfake
Qiu, H., Du, Y., & Lu, T. (2022) · Future Internet · doi:10.3390/fi14020046 · Cited by 5 · see citing works
Impact of Deepfake Technology on FinTech Applications
Abbas, N. N., Ahmad, R., Qazi, S., & Ahmed, W. (2022) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-6684-5284-4.ch012 · Cited by 5 · see citing works
CDNet: Cluster Decision for Deepfake Detection Generalization
Hou, Z., Hua, Z., Zhang, K., & Zhang, Y. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10223180 · Cited by 5 · see citing works
Multi-source Deep Domain Adaptation for Deepfake Detection
Seraj, M. S., & Chakraborty, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78305-0_9 · Cited by 4 · see citing works
Deepfake Detection: Emerging Techniques and Evolving Challenges
Natarajan, K., Mathur, A. K., Bhargava, K., Singh, M., & Tejpal, M. (2024) · International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON) · doi:10.1109/iemecon62401.2024.10846677 · Cited by 4 · see citing works
Detection of Compressed DeepFake Video Drawbacks and Technical Developments
Humidan, A.-S., Abdullah, L. N., & Halin, A. B. A. (2022) · International Conference on Signal Processing and Information Security (ICSPIS) · doi:10.1109/icspis57063.2022.10002433 · Cited by 4 · see citing works
TFD-Video: Threshold-Aware Federated Deepfake Detection for Video Forensics
Al-Fehani, M., Albaseer, A. M., & Al-Kuwari, S. (2026) · IEEE Access · doi:10.1109/access.2026.3660914 · Cited by 3 · see citing works
Evading Deepfake Detectors via High Quality Face Pre-Processing Methods
Kim, J., Kim, T., Kim, J., & Woo, S. S. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956520 · Cited by 3 · see citing works
Self-citationExploring the Impact of Moire Pattern on Deepfake Detectors
Tariq, R., Tariq, S., & Woo, S. S. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647902 · Cited by 3 · see citing works
Self-citationCountering Deepfakes using an Improved Advanced CNN and its Ensemble with Pretrained Models
Mathur, A. K., Bhargava, K., Singh, M., Tejpal, M., & Natarajan, K. (2024) · International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) · doi:10.1109/iceeict61591.2024.10718622 · Cited by 3 · see citing works
Jędrasiak, K. (2025) · Bezpieczeństwo i Technika Pożarnicza · doi:10.12845/sft.66.2.2025.10 · Cited by 2 · see citing works
Beyond Frames: 3D-CoAtNet for Generalizable Deepfake Video Detection
Alattas, E., Clark, J., Alsulami, B. S., & Jarraya, S. K. (2026) · IEEE Access · doi:10.1109/access.2026.3666623 · Cited by 3 · see citing works
James, U. U., Olarinoye, H. S., Uchenna, I. R., Idika, C. N., Ngene, O. J., Ijiga, O. M., & Itemuagbor, K. (2025) · Advances in Artificial Intelligence and Robotics Research · doi:10.4236/airr.2025.11004 · Cited by 2 · see citing works
Synergizing CNNs: An Ensemble Framework for Enhanced Image Classification
Aryan, R., Gupta, A., Tiwari, A., & Leema, A. A. (2024) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac61858.2024.10714623 · Cited by 1 · see citing works
Media Forensics and Deepfake-Systematic Survey
Nadeem Jabbar, C. H., Saghir, A., Meer, A. A., Sahi, S., Hassan, B., & Yasir, S. M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-77617-5_24 · Cited by 6 · see citing works
Perotti, A., Nurisso, M., & Zaffaroni, M. (2025) · doi:10.1145/3746265.3759659 · Cited by 1 · see citing works
Swin-BiLSTM with Attention for Face Swap Detection in Deepfake Videos
Lukitania, N. F., & Suryani, V. (2025) · doi:10.1109/icoict66265.2025.11192953 · Cited by 1 · see citing works
Seeing Through the Blur: Unlocking Defocus Maps for Deepfake Detection
Jeon, M., & Woo, S. S. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761260 · Cited by 1 · see citing works
Self-citationUnsupervised Domain Adaptation-Based Cross-Type Deepfake Image Detection
Wang, Q., Wang, X., Liu, Z., Bai, N., Zhao, M., & Pang, S. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3685445 · Cited by 1 · see citing works
CMUA++: A cross-model universal active defense framework for combating deepfake
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Recent Trends in Deepfake Detection
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Woo, S. S., Tariq, S., & Kim, H. (2022) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3488932.3517204 · Cited by 1 · see citing works
Self-citationNegative Adversarial Example Generation Against Naver's Celebrity Recognition API
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Deepfakes in Videoverhandlungen vor Gericht
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GANK: Dynamic Geometric and Appearance Features for Efficient and Robust Detection of Face Forgery
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Além do Desempenho: Um Estudo da Confiabilidade de Detectores de Deepfakes
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Combating Deepfakes: A Powerful Hybrid Approach with LSTM and ResNeXt
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Exploring a deepfake attack: a multi-dimensional approach
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AI-Driven Detection of Face-Swapped Deepfake Videos Using Machine Learning Techniques
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Nayak, P., Vaishnavi, B. S., & Kumar, L. K. S. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21912-4
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Multi-target Deep Domain Adaptation for Deepfake Detection
Seraj, M. S., & Chakraborty, S. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728549
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- Authors
- Tariq, S., Lee, S., & Woo, S.
- Affiliations
- Sungkyunkwan University (KR)
- Venue
- Proceedings of the Web Conference 2021
- Publisher
- ACM
- Published
- 2021-04-19
- Pages
- 3625-3637
- OpenAlex
- W3158734383
- Cited by
- 91
- References
- 54
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 19 · 2023: 16 · 2022: 24 · 2021: 7
- FWCI
- 5.40
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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, video manipulation, convolutional LSTM, ResNet, temporal information
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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.
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