Deepfake generation and detection, a survey
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Current detection methods' reliability and efficiency are insufficient, especially in deepfake video detection.
“Current detection methods' reliability and efficiency are insufficient, especially in the case of deepfake video detection (Zhang 2022).”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Zhang 2022
Standard benchmark datasets are needed for deepfake detection because current datasets have varied resolutions, small video lengths, and a lack of variety.
“According to Zhang (2022), standard benchmark datasets are needed for deepfake detection because current datasets have varied resolutions (for images and videos), small video lengths, and a lack of variety.”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Zhang 2022
Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714 · see citing works
StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., & Choo, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00916 · Cited by 4,163 · see citing works
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics
Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00327 · Cited by 1,846 · cited 11× · see citing works
“Fortunately, there are also some deep-fake related datasets released since 2017, including UADFV [58], FaceForensics [80], FaceForensics++ [81], DeepfakeTIMIT [53], and Celeb-DF [60].”
MethodInfluentialDeep Fakes: A Looming Challenge for Privacy, Democracy, and National Security
Chesney, R., & Citron, D. K. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3213954 · Cited by 880 · cited 1× · see citing works
“These realistic deepfake medias have been a significant threat to privacy, democracy, national security, and society [5, 13].”
BackgroundDeepFakes: a New Threat to Face Recognition? Assessment and Detection
Korshunov, P., & Marcel, S. (2018) · arXiv · doi:10.48550/arxiv.1812.08685 · Cited by 497 · see citing works
Deepfake Video Detection through Optical Flow Based CNN
Amerini, I., Galteri, L., Caldelli, R., & Del Bimbo, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00152 · Cited by 424 · cited 1× · see citing works
“In [7], CNN classifiers are used to learn inter-frame dissimilarities.”
MethodDetecting GAN generated Fake Images using Co-occurrence Matrices
Nataraj, L., Mohammed, T. M., Manjunath, B. S., Chandrasekaran, S., Flenner, A., Bappy, J. H., & Roy-Chowdhury, A. K. (2019) · Electronic Imaging · doi:10.2352/issn.2470-1173.2019.5.mwsf-532 · Cited by 331 · see citing works
MFC Datasets: Large-Scale Benchmark Datasets for Media Forensic Challenge Evaluation
Guan, H., Kozak, M., Robertson, E., Lee, Y., Yates, A. N., Delgado, A., Zhou, D., Kheyrkhah, T., Smith, J., & Fiscus, J. (2019) · 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2019.00018 · Cited by 329 · see citing works
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method using Affective Cues
Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413570 · Cited by 319 · see citing works
OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder
Khalid, H., & Woo, S. S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00336 · Cited by 197 · see citing works
Deepfakes Detection with Automatic Face Weighting
Montserrat, D. M., Hao, H., Yarlagadda, S. K., Baireddy, S., Shao, R., Horvath, J., Bartusiak, E., Yang, J., Guera, D., Zhu, F., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00342 · Cited by 181 · see citing works
DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection
Rana, M. S., & Sung, A. H. (2020) · 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom) · doi:10.1109/cscloud-edgecom49738.2020.00021 · Cited by 133 · see citing works
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Masood, M., Nawaz, M., Malik, K. M., Javed, A., Irtaza, A., & Malik, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03766-z · Cited by 543 · see citing works
Deepfake video detection: challenges and opportunities
Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10810-6 · Cited by 214 · see citing works
“According to Zhang (2022), standard benchmark datasets are needed for deepfake detection because current datasets have varied resolutions (for images and videos), small video lengths, and a lack of variety.”
Deepfake 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 · see citing works
A Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats
Mubarak, R., Alsboui, T., Alshaikh, O., Inuwa-Dutse, I., Khan, S., & Parkinson, S. (2023) · IEEE Access · doi:10.1109/access.2023.3344653 · Cited by 132 · see citing works
Deepfake detection using convolutional vision transformers and convolutional neural networks
Soudy, A. H., Sayed, O., Tag-Elser, H., Ragab, R., Mohsen, S., Mostafa, T., Abohany, A. A., & Slim, S. O. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10181-7 · Cited by 129 · see citing works
Deepfakes generation and detection: a short survey
Akhtar, Z. (2023) · Journal of Imaging · doi:10.3390/jimaging9010018 · Cited by 106 · see citing works
GAN-Generated Faces Detection: A Survey and New Perspectives
Wang, X., Guo, H., Hu, S., Chang, M.-C., & Lyu, S. (2023) · Frontiers in Artificial Intelligence and Applications · doi:10.3233/faia230558 · Cited by 98 · see citing works
Warning: Humans cannot reliably detect speech deepfakes
Mai, K. T., Bray, S., Davies, T., & Griffin, L. D. (2023) · PLOS ONE · doi:10.1371/journal.pone.0285333 · Cited by 98 · see citing works
“We refer the reader to Zhang (2022) [19] for further information on deepfake terminology.”
Deepfake Detection by Exploiting Surface Anomalies: The Surfake Approach
Ciamarra, A., Caldelli, R., Becattini, F., Seidenari, L., & Del Bimbo, A. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00112 · Cited by 27 · see citing works
Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization
Katamneni, V. S., & Rattani, A. (2024) · 2024 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb62174.2024.10744480 · Cited by 20 · see citing works
Quality-based Artifact Modeling for Facial Deepfake Detection in Videos
Concas, S., La Cava, S. M., Casula, R., Orrù, G., Puglisi, G., & Marcialis, G. L. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00389 · Cited by 12 · see citing works
- Authors
- Zhang, T.
- Affiliations
- Guilin University of Electronic Technology (CN)
- Corresponding author
- Zhang, Tao
- ORCID
- Zhang, Tao
- Venue
- Multimedia Tools and Applications
- Published
- 2022-01-08
- Volume
- 81
- Issue
- 5
- Pages
- 6259-6276
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- OpenAlex
- W4205695807
- Cited by
- 176
- References
- 13
- Citations by year
- 2026: 39 · 2025: 54 · 2024: 51 · 2023: 25 · 2022: 7
- FWCI
- 11.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Multimedia Tools and Applications (publishedVersion)
- Funders
- Guangxi Key Laboratory of Cryptography and Information Security (GCIS201806)
- Guangxi Key Laboratory of Trusted Software (kx202016)
- Key Lab of Film and TV Media Technology of Zhejiang Province (2020E10015)
- Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province (obdma202001)
- Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities (GXIC20-03)
- 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 generation, deepfake detection, face swapping, deepfake datasets, national security
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