Overview of Technologies for Detecting Modified Content of the DeepFake Class
Abstract
The article provides an overview of publications on the main technologies for detecting modified content
of the DeepFake class, including a list of publicly available datasets and the results of testing solutions
available in the public domain. The article also presents the results of independent testing of
DeepFake detection systems, obtained during the DFDC ‒ 2019 (DeepFakeDetectionContest) and gives
brief reviews of similar competitions held in 2020-2021 in China. The article also describes a new approach
to detecting photo/video materials created using DeepFake technologies. The authors' analysis
of the solutions made it possible to create a new way of detecting fake content, which was patented
by the authors in the Federal Service for Intellectual Property for Technology. The DeepFake detection
mechanism proposed by the authors has a number of significant advantages over the solutions
described in the review, which allows us to count on more efficient detection of attempts to bypass
biometric systems.
References
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4. Rössler A., Cozzolino D., Verdoliva L., Riess C., Thies J., Niessner M. FaceForensics++: Learning to Detect Manipulated Facial Images. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South): IEEE Computer Society; 2019. p. 1-11. doi: https://doi.org/10.1109/ICCV.2019.00009
5. Li Y., Yang X., Sun P., Qi H., Lyu S. Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA: IEEE Computer Society; 2020. p. 3204-3213. doi: https://doi.org/10.1109/CVPR42600.2020.00327
6. Dolhansky B., Howes R., Pflaum B., Baram N., Ferrer C.C. The Deepfake Detection Challenge (DFDC) Preview Dataset. arXiv:1910.08854. 2019. doi: https://doi.org/10.48550/arXiv.1910.08854
7. Matern F., Riess C., Stamminger M. Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations. In: 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW). Waikoloa, HI, USA: IEEE Computer Society; 2019. p. 83-92. doi: https://doi.org/10.1109/WACVW.2019.00020
8. Yang X., Li Y., Lyu S. Exposing Deep Fakes Using Inconsistent Head Poses. In: ICASSP 2019 ‒ 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton, UK: IEEE Computer Society; 2019. p. 8261-8265. doi: https://doi.org/10.1109/ICASSP.2019.8683164
9. Agarwal S., Farid H. Protecting World Leaders Against Deep Fakes. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Long Beach, CA, USA; 2019. p. 1-8. Available at: https://farid.berkeley.edu/downloads/publications/cvpr19/cvpr19a.pdf (accessed 03.09.2022).
10. Jung T., Kim S., Kim K. DeepVision: Deepfakes Detection Using Human Eye Blinking Pattern. IEEE Access. 2020;8:83144-83154. doi: https://doi.org/10.1109/ACCESS.2020.2988660
11. Li Y., Lyu S. Exposing DeepFake Videos By Detecting Face Warping Artifacts. arXiv:1811.00656. 2019. doi: https://doi.org/10.48550/arXiv.1811.00656
12. Afchar D., Nozick V., Yamagishi J., Echizen I. MesoNet: a Compact Facial Video Forgery Detection Network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS). Hong Kong, China: IEEE Computer Society; 2018. p. 1-7. doi: https://doi.org/10.1109/WIFS.2018.8630761
13. Zhou P., Han X., Morariu V.I., Davis L.S. Two-Stream Neural Networks for Tampered Face Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, HI, USA: IEEE Computer Society; 2017. p. 1831-1839. doi: https://doi.org/10.1109/CVPRW.2017.229
14. Nguyen H.Y., Fang F., Yamagishi J., Echizen I. Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos. In: 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS). Tampa, FL, USA: IEEE Computer Society; 2019. p. 1-8. doi: https://doi.org/10.1109/BTAS46853.2019.9185974
15. Nguyen H.H., Yamagishi J., Echizen I. Use of a Capsule Network to Detect Fake Images and Videos. arXiv:1910.12467. 2019. doi: https://doi.org/10.48550/arXiv.1910.12467
16. Dang H., Liu F., Stehouwer J., Liu X., Jain A. On the Detection of Digital Face Manipulation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA: IEEE Computer Society; 2020. p. 5780-5789. doi: https://doi.org/10.1109/CVPR42600.2020.00582
17. Wang Y., Dantcheva A. A Video is Worth More than 1000 Lies. Comparing 3DCNN Approaches for Detecting Deepfakes. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020). Buenos Aires, Argentina: IEEE Computer Society; 2020. p. 515-519. doi: https://doi.org/10.1109/FG47880.2020.00089
18. Güera D., Delp E. Deepfake Video Detection Using Recurrent Neural Networks. In: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). Auckland, New Zealand: IEEE Computer Society; 2018. p. 1-6. doi: https://doi.org/10.1109/AVSS.2018.8639163
19. Sabir E., Cheng J., Jaiswal A., AbdAlmageed W., Masi I., Natarajan P. Recurrent Convolutional Strategies for Face Manipulation Detection in Videos. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops'2019). Long Beach, CA, USA: Computer Vision Foundation; 2019. p. 80-87. doi: https://doi.org/10.48550/arXiv.1905.00582
20. Tolosana R., Romero-Tapiador S., Fierrez J., Vera-Rodriguez R. DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance. In: Del Bimbo A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science. Vol. 12665. Cham: Springer; 2021. p. 442-456. doi: https://doi.org/10.1007/978-3-030-68821-9_38
21. Wang R., Juefei-Xu F., Ma L., Xie X., Huang Y., Wang J., Liu Y. FakeSpotter: a simple yet robust baseline for spotting AI-synthesized fake faces. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI'20). Yokohama, Yokohama, Japan; 2021. Article number: 476. p. 3444-3451. Available at: https://dl.acm.org/doi/pdf/10.5555/3491440.3491916 (accessed 03.09.2022).
22. Nataraj L., Mohammed T.M., Manjunath B.S., Chandrasekaran S., Flenner A., Bappy J.H., Roy-Chowdhury A.K. Detecting GAN generated Fake Images using Co-occurrence Matrices. In: Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics. IS&T; 2019. p. 532. doi: https://doi.org/10.2352/ISSN.2470-1173.2019.5.MWSF-532
23. Bharati A., Singh R., Vatsa M., Bowyer K.W. Detecting Facial Retouching Using Supervised Deep Learning. IEEE Transactions on Information Forensics and Security. 2016;11(9):1903-1913. doi: https://doi.org/10.1109/TIFS.2016.2561898
24. Jain A., Singh R., Vatsa M. On Detecting GANs and Retouching based Synthetic Alterations. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). Redondo Beach, CA, USA: IEEE Computer Society; 2018. p. 1-7. doi: https://doi.org/10.1109/BTAS.2018.8698545
25. Tariq S., Lee S., Kim H., Shin Y., Woo S.S. Detecting Both Machine and Human Created Fake Face Images In the Wild. In: Proceedings of the 2nd International Workshop on Multimedia Privacy and Security (MPS'18). New York, NY, USA: Association for Computing Machinery; 2018. p. 81-87. doi: https://doi.org/10.1145/3267357.3267367
26. Wang S., Wang O., Owens A., Zhang R., Efros A. Detecting Photoshopped Faces by Scripting Photoshop. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South): IEEE Computer Society; 2019. p. 10071-10080. doi: https://doi.org/10.1109/ICCV.2019.01017
27. Marra F., Saltori C., Boato G., Verdoliva L. Incremental learning for the detection and classification of GAN-generated images. In: 2019 IEEE International Workshop on Information Forensics and Security (WIFS). Delft, Netherlands: IEEE Computer Society; 2019. p. 1-6. doi: https://doi.org/10.1109/WIFS47025.2019.9035099
28. Zhang X., Karaman S., Chang S.-F. Detecting and Simulating Artifacts in GAN Fake Images. In: 2019 IEEE International Workshop on Information Forensics and Security (WIFS). Delft, Netherlands: IEEE Computer Society; 2019. p. 1-6. doi: https://doi.org/10.1109/WIFS47025.2019.9035107
29. Rathgeb C., Botaljov A., Stockhardt F., Isadskiy S., Debiasi L., Uhl A., Busch C. PRNU-based Detection of Facial Retouching. IET Biometrics. 2020;9(4):154-164. doi: https://doi.org/10.1049/iet-bmt.2019.0196
30. Amerini I., Galteri L., Caldelli R., Del Bimbo A. Deepfake Video Detection through Optical Flow Based CNN. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Seoul, Korea (South): IEEE Computer Society; 2019. p. 1205-1207. doi: https://doi.org/10.1109/ICCVW.2019.00152
31. Peng B. et al. DFGC 2021: A DeepFake Game Competition. In: 2021 IEEE International Joint Conference on Biometrics (IJCB). Shenzhen, China: IEEE Computer Society; 2021. p. 1-8. doi: https://doi.org/10.1109/IJCB52358.2021.9484387
32. Peng B. et al. DFGC 2022: The Second DeepFake Game Competition. In: 2022 IEEE International Joint Conference on Biometrics (IJCB). Abu Dhabi, United Arab Emirates: IEEE Computer Society; 2022. p. 1-10. doi: https://doi.org/10.1109/IJCB54206.2022.10007991
2. Li Y., Chang M.-C., Lyu S. In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS). Hong Kong, China: IEEE Computer Society; 2018. p. 1-7. doi: https://doi.org/10.1109/WIFS.2018.8630787
3. Korshunov P., Marcel S. Deepfakes: a New Threat to Face Recognition? Assessment and Detection. arXiv:1812.08685. 2018. doi: https://doi.org/10.48550/arXiv.1812.08685
4. Rössler A., Cozzolino D., Verdoliva L., Riess C., Thies J., Niessner M. FaceForensics++: Learning to Detect Manipulated Facial Images. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South): IEEE Computer Society; 2019. p. 1-11. doi: https://doi.org/10.1109/ICCV.2019.00009
5. Li Y., Yang X., Sun P., Qi H., Lyu S. Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA: IEEE Computer Society; 2020. p. 3204-3213. doi: https://doi.org/10.1109/CVPR42600.2020.00327
6. Dolhansky B., Howes R., Pflaum B., Baram N., Ferrer C.C. The Deepfake Detection Challenge (DFDC) Preview Dataset. arXiv:1910.08854. 2019. doi: https://doi.org/10.48550/arXiv.1910.08854
7. Matern F., Riess C., Stamminger M. Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations. In: 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW). Waikoloa, HI, USA: IEEE Computer Society; 2019. p. 83-92. doi: https://doi.org/10.1109/WACVW.2019.00020
8. Yang X., Li Y., Lyu S. Exposing Deep Fakes Using Inconsistent Head Poses. In: ICASSP 2019 ‒ 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton, UK: IEEE Computer Society; 2019. p. 8261-8265. doi: https://doi.org/10.1109/ICASSP.2019.8683164
9. Agarwal S., Farid H. Protecting World Leaders Against Deep Fakes. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Long Beach, CA, USA; 2019. p. 1-8. Available at: https://farid.berkeley.edu/downloads/publications/cvpr19/cvpr19a.pdf (accessed 03.09.2022).
10. Jung T., Kim S., Kim K. DeepVision: Deepfakes Detection Using Human Eye Blinking Pattern. IEEE Access. 2020;8:83144-83154. doi: https://doi.org/10.1109/ACCESS.2020.2988660
11. Li Y., Lyu S. Exposing DeepFake Videos By Detecting Face Warping Artifacts. arXiv:1811.00656. 2019. doi: https://doi.org/10.48550/arXiv.1811.00656
12. Afchar D., Nozick V., Yamagishi J., Echizen I. MesoNet: a Compact Facial Video Forgery Detection Network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS). Hong Kong, China: IEEE Computer Society; 2018. p. 1-7. doi: https://doi.org/10.1109/WIFS.2018.8630761
13. Zhou P., Han X., Morariu V.I., Davis L.S. Two-Stream Neural Networks for Tampered Face Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, HI, USA: IEEE Computer Society; 2017. p. 1831-1839. doi: https://doi.org/10.1109/CVPRW.2017.229
14. Nguyen H.Y., Fang F., Yamagishi J., Echizen I. Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos. In: 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS). Tampa, FL, USA: IEEE Computer Society; 2019. p. 1-8. doi: https://doi.org/10.1109/BTAS46853.2019.9185974
15. Nguyen H.H., Yamagishi J., Echizen I. Use of a Capsule Network to Detect Fake Images and Videos. arXiv:1910.12467. 2019. doi: https://doi.org/10.48550/arXiv.1910.12467
16. Dang H., Liu F., Stehouwer J., Liu X., Jain A. On the Detection of Digital Face Manipulation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA: IEEE Computer Society; 2020. p. 5780-5789. doi: https://doi.org/10.1109/CVPR42600.2020.00582
17. Wang Y., Dantcheva A. A Video is Worth More than 1000 Lies. Comparing 3DCNN Approaches for Detecting Deepfakes. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020). Buenos Aires, Argentina: IEEE Computer Society; 2020. p. 515-519. doi: https://doi.org/10.1109/FG47880.2020.00089
18. Güera D., Delp E. Deepfake Video Detection Using Recurrent Neural Networks. In: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). Auckland, New Zealand: IEEE Computer Society; 2018. p. 1-6. doi: https://doi.org/10.1109/AVSS.2018.8639163
19. Sabir E., Cheng J., Jaiswal A., AbdAlmageed W., Masi I., Natarajan P. Recurrent Convolutional Strategies for Face Manipulation Detection in Videos. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops'2019). Long Beach, CA, USA: Computer Vision Foundation; 2019. p. 80-87. doi: https://doi.org/10.48550/arXiv.1905.00582
20. Tolosana R., Romero-Tapiador S., Fierrez J., Vera-Rodriguez R. DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance. In: Del Bimbo A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science. Vol. 12665. Cham: Springer; 2021. p. 442-456. doi: https://doi.org/10.1007/978-3-030-68821-9_38
21. Wang R., Juefei-Xu F., Ma L., Xie X., Huang Y., Wang J., Liu Y. FakeSpotter: a simple yet robust baseline for spotting AI-synthesized fake faces. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI'20). Yokohama, Yokohama, Japan; 2021. Article number: 476. p. 3444-3451. Available at: https://dl.acm.org/doi/pdf/10.5555/3491440.3491916 (accessed 03.09.2022).
22. Nataraj L., Mohammed T.M., Manjunath B.S., Chandrasekaran S., Flenner A., Bappy J.H., Roy-Chowdhury A.K. Detecting GAN generated Fake Images using Co-occurrence Matrices. In: Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics. IS&T; 2019. p. 532. doi: https://doi.org/10.2352/ISSN.2470-1173.2019.5.MWSF-532
23. Bharati A., Singh R., Vatsa M., Bowyer K.W. Detecting Facial Retouching Using Supervised Deep Learning. IEEE Transactions on Information Forensics and Security. 2016;11(9):1903-1913. doi: https://doi.org/10.1109/TIFS.2016.2561898
24. Jain A., Singh R., Vatsa M. On Detecting GANs and Retouching based Synthetic Alterations. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). Redondo Beach, CA, USA: IEEE Computer Society; 2018. p. 1-7. doi: https://doi.org/10.1109/BTAS.2018.8698545
25. Tariq S., Lee S., Kim H., Shin Y., Woo S.S. Detecting Both Machine and Human Created Fake Face Images In the Wild. In: Proceedings of the 2nd International Workshop on Multimedia Privacy and Security (MPS'18). New York, NY, USA: Association for Computing Machinery; 2018. p. 81-87. doi: https://doi.org/10.1145/3267357.3267367
26. Wang S., Wang O., Owens A., Zhang R., Efros A. Detecting Photoshopped Faces by Scripting Photoshop. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South): IEEE Computer Society; 2019. p. 10071-10080. doi: https://doi.org/10.1109/ICCV.2019.01017
27. Marra F., Saltori C., Boato G., Verdoliva L. Incremental learning for the detection and classification of GAN-generated images. In: 2019 IEEE International Workshop on Information Forensics and Security (WIFS). Delft, Netherlands: IEEE Computer Society; 2019. p. 1-6. doi: https://doi.org/10.1109/WIFS47025.2019.9035099
28. Zhang X., Karaman S., Chang S.-F. Detecting and Simulating Artifacts in GAN Fake Images. In: 2019 IEEE International Workshop on Information Forensics and Security (WIFS). Delft, Netherlands: IEEE Computer Society; 2019. p. 1-6. doi: https://doi.org/10.1109/WIFS47025.2019.9035107
29. Rathgeb C., Botaljov A., Stockhardt F., Isadskiy S., Debiasi L., Uhl A., Busch C. PRNU-based Detection of Facial Retouching. IET Biometrics. 2020;9(4):154-164. doi: https://doi.org/10.1049/iet-bmt.2019.0196
30. Amerini I., Galteri L., Caldelli R., Del Bimbo A. Deepfake Video Detection through Optical Flow Based CNN. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Seoul, Korea (South): IEEE Computer Society; 2019. p. 1205-1207. doi: https://doi.org/10.1109/ICCVW.2019.00152
31. Peng B. et al. DFGC 2021: A DeepFake Game Competition. In: 2021 IEEE International Joint Conference on Biometrics (IJCB). Shenzhen, China: IEEE Computer Society; 2021. p. 1-8. doi: https://doi.org/10.1109/IJCB52358.2021.9484387
32. Peng B. et al. DFGC 2022: The Second DeepFake Game Competition. In: 2022 IEEE International Joint Conference on Biometrics (IJCB). Abu Dhabi, United Arab Emirates: IEEE Computer Society; 2022. p. 1-10. doi: https://doi.org/10.1109/IJCB54206.2022.10007991
Published
2022-10-24
How to Cite
BALASHOV, Aleksandr Victorovich et al.
Overview of Technologies for Detecting Modified Content of the DeepFake Class.
Modern Information Technologies and IT-Education, [S.l.], v. 18, n. 3, p. 680-690, oct. 2022.
ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/920>. Date accessed: 26 apr. 2025.
doi: https://doi.org/10.25559/SITITO.18.202203.680-690.
Section
Theoretical and Practical Aspects of Cybersecurity
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