The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Deepfake Video Detection through Optical Flow Based CNN
Open accessAbstract
Recent advances in visual media technology have led to new tools for processing and, above all, generating multimedia contents. In particular, modern AI-based technologies have provided easy-to-use tools to create extremely realistic manipulated videos. Such synthetic videos, named Deep Fakes, may constitute a serious threat to attack the reputation of public subjects or to address the general opinion on a certain event. According to this, being able to individuate this kind of fake information becomes fundamental. In this work, a new forensic technique able to discern between fake and original video sequences is given; unlike other state-of-the-art methods which resorts at single video frames, we propose the adoption of optical flow fields to exploit possible inter-frame dissimilarities. Such a clue is then used as feature to be learned by CNN classifiers. Preliminary results obtained on FaceForensics++ dataset highlight very promising performances.
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“the re- cent common underlying mechanism for deepfake cre- ation is deep learning models such as autoencoders and generative adversarial networks (GANs)”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [7]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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“Amerini etal. (2019) have used CNN with optical flow to differentiate between fake and real videos.”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Amerini et al. 2019
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
FaceForensics++: Learning to Detect Manipulated Facial Images
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00009 · Cited by 3,314
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Sun, D., Yang, X., Liu, M.-Y., & Kautz, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00931 · Cited by 2,801
MesoNet: a Compact Facial Video Forgery Detection Network
Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018) · 2018 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2018.8630761 · Cited by 1,872
The computation of optical flow
Beauchemin, S. S., & Barron, J. A. (1995) · ACM Computing Surveys · doi:10.1145/212094.212141 · Cited by 1,313
In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking
Li, Y., Chang, M.-C., & Lyu, S. (2018) · 2018 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2018.8630787 · Cited by 1,089
Kim, H., Garrido, P., Tewari, A., Xu, W., Thies, J., Nießner, M., Pérez, P., Richardt, C., Zollhöfer, M., & Theobalt, C. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201283 · Cited by 702
Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., & Nießner, M. (2016) · ACM SIGGRAPH Emerging Technologies · doi:10.1145/2929464.2929475 · Cited by 589
Cited by
Sorted by citations
Deepfakes and beyond: A Survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.014 · Cited by 1,237
“Finally, in [101], Amerini et al. proposed the adoption of optical flow fields to exploit possible inter-frame dissimilarities.”
BackgroundMedia Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979
“[190] the optical flow field is estimated to exploit discrepancies in motion across frames, but only some preliminary results are presented.”
BackgroundThinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues
Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058
“Following previous face forgery detection methods [51,19,40,5], we conduct our experiments on the challenging FaceForensics++ [50] dataset.”
BackgroundMethodThe Creation and Detection of Deepfakes: A Survey
Mirsky, Y., & Lee, W. (2022) · ACM Computing Surveys · doi:10.1145/3425780 · Cited by 863
“In [25] a classier is trained pairs of sequential frames and in [11] the authors rene the network’s focus by monitoring the frames’ optical ow.”
The Creation and Detection of Deepfakes: A Survey
Mirsky, Y., & Lee, W. (2020) · arXiv · doi:10.48550/arxiv.2004.11138 · Cited by 863
Metadata
- Authors
- Amerini, I., Galteri, L., Caldelli, R., & Del Bimbo, A.
- Affiliations
- University of Florence (IT)
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (IT)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
- Publisher
- IEEE
- Published
- 2019-10
- Volume
- 2019
- Pages
- 1205-1207
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2995516027
- Cited by
- 424
- References
- 14
- Citations by year
- 2026: 35 · 2025: 70 · 2024: 77 · 2023: 95 · 2022: 71 · 2021: 49 · 2020: 27
- FWCI
- 11.98
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, other-oa)
- IEEE Conference Proceedings
- 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, optical flow, convolutional neural networks, manipulated video, video forensics, FaceForensics++, fake information
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