The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Detecting Deepfake Videos via Frame Serialization Learning
Abstract
Deepfake, a video forgery technique based on Generative Adversarial Network (GAN), has been proved to be a serious threat to the public security. The images and videos generated with it can even fool human eyes. In this paper, we propose a deep learning-based method to hunt Deepfake videos. A 3D-ResNext based model is developed to effectively learn the leverageable difference between fake videos and benign ones from multiple serialized frames. Furthermore, to address the information loss in compressed videos, the data enhancement technique is introduced in data preprocessing to collect sufficient training samples from public datasets, e.g., FaceForensics++, DeepFakeDetection and DFDC. The experiments demonstrated that our method has good performance and generalization ability in the task of Deepfake videos detection.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Several prior studies on detecting deepfake images or videos were focused on DL techniques, which could be split into two detection methods: Conventional Neural Networks (CNN)-based methods and Region Conventional Neural Networks (RCNN)-based methods”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as X. Zhou et al., 2020
Citations
Cites
Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
Hara, K., Kataoka, H., & Satoh, Y. (2017) · 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw.2017.373 · Cited by 726
The Deepfake Detection Challenge (DFDC) Preview Dataset
Dolhansky, B., Howes, R., Pflaum, B., Baram, N., & Ferrer, C. C. (2019) · arXiv · doi:10.48550/arxiv.1910.08854 · Cited by 413
Detection of GAN-Generated Fake Images over Social Networks
Marra, F., Gragnaniello, D., Cozzolino, D., & Verdoliva, L. (2018) · 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00084 · Cited by 399
Face swapping: automatically replacing faces in photographs
Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., & Nayar, S. K. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360638 · Cited by 333
Research works printed but not linked: 20
- xunyu, 0, Exposing image splicing with inconsistent local noise variances, IEEE International Conference on Computational Photography, 1
- 10.1145/3206004.3206009 · DOI
- 10.3390/app8122610 · DOI
- 10.1109/WIFS.2018.8630761 · DOI · Paper page
- andreas, 0, FaceForensics++: Learning to detect manipulated facial images, In IEEE International Conf on Computer Vision, 1
- wei, 2014, Exploring DCT coefficient quantization effects for local tampering detection, Ieee Transactions on Information Forensics and Security IEEE Trans, 9, 1653, 10.1109/TIFS.2014.2345479 · DOI
- davide, 0, Splicebuster: A new blind image splicing detector, IEEE International Workshop on Information Forensics and Security, 16
- agarwal, 2018, Swapped! Digital face presentation attack detection via weighted local magnitude pattern, IEEE International Joint Conference on Biometrics, 659
- xinyi, 2020, Swapped face detection using deep learning and subjective assessment, EURASIP Journal on Information Security, 2020, 1
- 10.1145/1409060.1409074 · DOI
- 10.1145/97880.97906 · DOI
- goljan, 0, CFA-aware features for steganalysis of color images, Proceedings of SPIE - The International Society for Optical Engineering, 9409, 94090v
- 10.1145/1073204.1073209 · DOI
- robert, 2009, A picture's worth, Academic Emergency Medicine, 16, 178, 10.1111/j.1553-2712.2008.00335.x · DOI
- ian, 2014, Generative adversarial nets, Advances in neural information processing systems, 2672
- jawadul, 2017, Exploiting spatial structure for localizing manipulated image regions, IEEE International Conference on Computer Vision, 4980
- tiziano, 2012, Image forgery localization via block-grained analysis of JPEG artifacts, IEEE Transactions on Information Forensics and Security, 7, 1003, 10.1109/TIFS.2012.2187516 · DOI
- kensho, 0, Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and imagenet?, IEEE Conf Computer Vision and Pattern Recognition, 6546
- jiahui, 2020, 3D RANs: 3D residual attention networks for action recognition, The Visual Computer International Journal of Computer Graphics, 36, 1261
- tao, 2019, See better before looking closer Weakly supervised data augmentation network for fine-grained visual classification
Cited by
Sorted by citations
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
“Several prior studies on detecting deepfake images or videos were focused on DL techniques, which could be split into two detection methods: Conventional Neural Networks (CNN)-based methods and Region Conventional Neural Networks (RCNN)-based methods (X. Zhou et al., 2020).”
Method
Metadata
- Authors
- Zhou, X., Wang, Y., & Wu, P.
- Affiliations
- Sichuan University (CN)
- China Information Technology Security Evaluation Center (CN)
- Venue
- 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI)
- Publisher
- IEEE
- Published
- 2020-11-28
- Pages
- 391-395
- OpenAlex
- W3128583751
- Cited by
- 10
- References
- 4
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 4 · 2022: 1
- FWCI
- 0.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference of Safe Production and Informatization (IICSPI) (publishedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (20XNA031)
- Renmin University of China
- 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 forgery, generative adversarial networks, compressed video, data augmentation, FaceForensics++, DFDC
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Detecting Deepfake Videos via Frame Serialization Learning. https://app.cochat.ai/papers/10.1109/iicspi51290.2020.9332419
- The paper
- Zhou, X., Wang, Y., & Wu, P. (2020). Detecting Deepfake Videos via Frame Serialization Learning. 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI). https://doi.org/10.1109/iicspi51290.2020.9332419
