FSGAN: Subject Agnostic Face Swapping and Reenactment
Open accessAbstract
We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, FSGAN is subject agnostic and can be applied to pairs of faces without requiring training on those faces. To this end, we describe a number of technical contributions. We derive a novel recurrent neural network (RNN)-based approach for face reenactment which adjusts for both pose and expression variations and can be applied to a single image or a video sequence. For video sequences, we introduce continuous interpolation of the face views based on reenactment, Delaunay Triangulation, and barycentric coordinates. Occluded face regions are handled by a face completion network. Finally, we use a face blending network for seamless blending of the two faces while preserving target skin color and lighting conditions. This network uses a novel Poisson blending loss which combines Poisson optimization with perceptual loss. We compare our approach to existing state-of-the-art systems and show our results to be both qualitatively and quantitatively superior.
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FSGAN is the first to simultaneously adjust the pose, expression, and identity variations for both a single image and a video sequence.
“It is also the first to simultaneously adjust the pose, expression, and identity variations for both a single image and a video sequence.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Nirkin et al. 2019
The Introduction says FSGAN works in images and videos and claims it is the first to simultaneously manipulate pose, expression, and identity *without requiring person-specific or pair-specific training*. The citing sentence omits that condition, making the priority claim broader than the paper's. (LLM, conf=0.86)
Experimental — accuracy not yet measuredFSGAN is a subject agnostic method that doesn't rely on the training of pairs of faces.
“Face swapping GAN (FSGAN) (Nirkin et al. 2019) is a subject agnostic method that doesn't rely on the training of pairs of faces.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Nirkin et al. 2019
Subject-agnostic methods can apply the trained model to any new face pairs without retraining on a specific subject.
“the subjectagnostic methods can apply the trained model to any new face pairs without retraining on a speciic subject”
From DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models (2024) · cites this paper as [49]
Subject-agnostic methods only need a source face image to generate near photo-realistic fake data based on different driving faces.
“the subject-agnostic methods only need a source face image to generate near photo-realistic fake data based on diferent driving faces”
From DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models (2024) · cites this paper as [49]
In ForgeryNet, fake images are manipulated using 15 different manipulations while videos are manipulated using only 8 of them.
“The fake images are manipulated using 15 different manipulations while the videos are manipulated using only 8 of them [27,28,29,30,31,32,33,34,35,36].”
From On the Generalization of Deep Learning Models in Video Deepfake Detection (2023) · cites this paper as [33]
The Datasets and processing section describes FaceForensics++ videos, not ForgeryNet. Searches for ForgeryNet and for the claimed counts of 15 image manipulations and 8 video manipulations found no passage addressing those counts in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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“s The term deep fake is often used to describe synthetically-generated images and videos (typically of people) generated from a range of dierent techniques including DeepFake FaceSwap [1] and FS-GAN [24], Neural Textures [29], Face2Face [31], and FaceSwaps [2]. The popular DeepFake FaceSwap software uses a generative…”
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Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions
Naitali, A., Ridouani, M., Salahdine, F., & Kaabouch, N. (2023) · Computers · doi:10.3390/computers12100216 · Cited by 117 · see citing works
Adversarial Threats to DeepFake Detection: A Practical Perspective
Neekhara, P., Dolhansky, B., Bitton, J., & Ferrer, C. C. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00103 · Cited by 117 · see citing works
FReeNet: Multi-Identity Face Reenactment
Zhang, J., Zeng, X., Wang, M., Pan, Y., Liu, L., Liu, Y., Ding, Y., & Fan, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00537 · Cited by 111 · 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
Das, S., Seferbekov, S., Datta, A., Islam, M. S., & Amin, M. R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00421 · Cited by 107 · see citing works
Deepfake Media Forensics: Status and Future Challenges
Amerini, I., Barni, M., Battiato, S., Bestagini, P., Boato, G., Bruni, V., Caldelli, R., De Natale, F., De Nicola, R., Guarnera, L., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11030073 · Cited by 113 · see citing works
Self-Supervised Video Forensics by Audio-Visual Anomaly Detection
Feng, C., Chen, Z., & Owens, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01011 · Cited by 101 · see citing works
Audio-Visual Person-of-Interest DeepFake Detection
Cozzolino, D., Pianese, A., Nießner, M., & Verdoliva, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00101 · Cited by 104 · see citing works
Le, T.-N., Nguyen, H. H., Yamagishi, J., & Echizen, I. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00996 · Cited by 97 · see citing works
Cai, Z., Stefanov, K., Dhall, A., & Hayat, M. (2022) · 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta56598.2022.10034605 · Cited by 99 · see citing works
A Robust Approach to Multimodal Deepfake Detection
Salvi, D., Liu, H., Mandelli, S., Bestagini, P., Zhou, W., Zhang, W., & Tubaro, S. (2023) · Journal of Imaging · doi:10.3390/jimaging9060122 · Cited by 96 · see citing works
Delving into the Local: Dynamic Inconsistency Learning for DeepFake Video Detection
Gu, Z., Chen, Y., Yao, T., Ding, S., Li, J., & Ma, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19955 · Cited by 93 · see citing works
Evading DeepFake Detectors via Adversarial Statistical Consistency
Hou, Y., Guo, Q., Huang, Y., Xie, X., Ma, L., & Zhao, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01181 · Cited by 76 · see citing works
Vizoso, Á., Vaz-Álvarez, M., & López-García, X. (2021) · Media and Communication · doi:10.17645/mac.v9i1.3494 · Cited by 72 · see citing works
Deepfake forensics analysis: An explainable hierarchical ensemble of weakly supervised models
Silva, S. H., Bethany, M., Votto, A. M., Scarff, I. H., Beebe, N., & Najafirad, P. (2022) · Forensic Science International: Synergy · doi:10.1016/j.fsisyn.2022.100217 · Cited by 66 · see citing works
Deepfake: definitions, performance metrics and standards, datasets, and a meta-review
Altuncu, E., Franqueira, V. N. L., & Li, S. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1400024 · Cited by 65 · see citing works
Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
Shuai, C., Zhong, J., Wu, S., Lin, F., Wang, Z., Ba, Z., Liu, Z., Cavallaro, L., & Ren, K. (2023) · Proceedings of the 31st ACM International Conference on Multimedia · doi:10.1145/3581783.3612386 · Cited by 55 · see citing works
ADD: Attention-Based DeepFake Detection Approach
Khormali, A., & Yuan, J.-S. (2021) · Big Data and Cognitive Computing · doi:10.3390/bdcc5040049 · Cited by 55 · see citing works
Assessment framework for deepfake detection in real-world situations
Lu, Y., & Ebrahimi, T. (2024) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-024-00621-8 · Cited by 60 · see citing works
“In particular, current deep learning-based face manipulation techniques [4, 5, 6, 7] are capable of changing the expression, attributes, and even identity of a human face image, the outcome of which refers to the popular term ‘Deepfake’.”
BackgroundSpoC: Spoofing Camera Fingerprints
Cozzolino, D., Thies, J., Rossler, A., Niesner, M., & Verdoliva, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00110 · Cited by 54 · see citing works
TI <sup>2</sup> Net: Temporal Identity Inconsistency Network for Deepfake Detection
Liu, B., Liu, B., Ding, M., Zhu, T., & Yu, X. (2023) · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00467 · Cited by 50 · see citing works
Khan, S. A., & Dang-Nguyen, D.-T. (2024) · IEEE Access · doi:10.1109/access.2023.3348450 · Cited by 53 · see citing works
Generalization of Forgery Detection With Meta Deepfake Detection Model
Tran, V.-N., Kwon, S.-G., Lee, S.-H., Le, H.-S., & Kwon, K.-R. (2023) · IEEE Access · doi:10.1109/access.2022.3232290 · Cited by 47 · see citing works
“FSGAN [18] proposed Face Swapping GAN, which derives a recurrent neural network (RNN) for face reenactment and adapts to changes in position and expression.”
MethodCross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection
Coccomini, D. A., Caldelli, R., Falchi, F., Gennaro, C., & Amato, G. (2022) · Proceedings of the 1st International Workshop on Multimedia AI against Disinformation · doi:10.1145/3512732.3533582 · Cited by 47 · see citing works
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues
Pan, K., Yin, Y., Wei, Y., Lin, F., Ba, Z., Liu, Z., Wang, Z., Cavallaro, L., & Ren, K. (2023) · Proceedings of the 31st ACM International Conference on Multimedia · doi:10.1145/3581783.3612377 · Cited by 37 · see citing works
MINTIME: Multi-Identity Size-Invariant Video Deepfake Detection
Coccomini, D. A., Zilos, G. K., Amato, G., Caldelli, R., Falchi, F., Papadopoulos, S., & Gennaro, C. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3409054 · Cited by 33 · see citing works
On the Generalization of Deep Learning Models in Video Deepfake Detection
Coccomini, D. A., Caldelli, R., Falchi, F., & Gennaro, C. (2023) · Journal of Imaging · doi:10.3390/jimaging9050089 · Cited by 32 · see citing works
Watch Those Words: Video Falsification Detection Using Word-Conditioned Facial Motion
Agarwal, S., Hu, L., Ng, E., Darrell, T., Li, H., & Rohrbach, A. (2023) · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00469 · Cited by 28 · see citing works
Multimodal Approach for DeepFake Detection
Lomnitz, M., Hampel-Arias, Z., Sandesara, V., & Hu, S. (2020) · 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) · doi:10.1109/aipr50011.2020.9425192 · Cited by 30 · see citing works
Identity-Driven DeepFake Detection
Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Chen, D., Wen, F., & Guo, B. (2020) · arXiv · doi:10.48550/arxiv.2012.03930 · Cited by 26 · see citing works
Narayan, K., Agarwal, H., Thakral, K., Mittal, S., Vatsa, M., & Singh, R. (2022) · 2022 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb54206.2022.10007968 · Cited by 26 · see citing works
FeatureTransfer: Unsupervised Domain Adaptation for Cross-Domain Deepfake Detection
Chen, B., & Tan, S. (2021) · Security and Communication Networks · doi:10.1155/2021/9942754 · Cited by 23 · see citing works
“(e fake videos in the DFDC-F dataset were created by different methods, including Deepfake Autoencoder (DFAE) [33], MM/NN face swap [34], NTH [35], and FSGAN [36].”
MethodFooled twice – people cannot detect deepfakes but think they can
Köbis, N., Doležalová, B., & Soraperra, I. (2021) · doi:10.2139/ssrn.3832978 · Cited by 194 · see citing works
DFGC 2021: A DeepFake Game Competition
Peng, B., Fan, H., Wang, W., Dong, J., Li, Y., Lyu, S., Li, Q., Sun, Z., Chen, H., Chen, B., et al. (2021) · 2021 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb52358.2021.9484387 · Cited by 23 · see citing works
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection
Zhu, H., Fu, C., Wu, Q., Wu, W., Qian, C., & He, R. (2020) · arXiv · doi:10.48550/arxiv.2011.02674 · Cited by 20 · see citing works
DeepFake detection method based on multi-scale interactive dual-stream network
Cheng, Z., Wang, Y., Wan, Y., & Jiang, C. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104263 · Cited by 19 · 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
DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models
Wang, L., Meng, X., Li, D., Zhang, X., Ji, S., & Guo, S. (2024) · ACM Transactions on Privacy and Security · doi:10.1145/3634914 · Cited by 11 · see citing works
Trusted Media Challenge Dataset and User Study
Chen, W., Chua, S. L. B., Winkler, S., & Ng, S.-K. (2022) · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · doi:10.1145/3511808.3557715 · Cited by 7 · see citing works
Attention-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
Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges
Liu, P., Tao, Q., & Zhou, J. T. (2024) · arXiv · doi:10.48550/arxiv.2406.06965 · Cited by 6 · see citing works
Anshul, A., Gopal, S., Rajan, D., & Chng, E. S. (2025) · 2025 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01283 · Cited by 1 · see citing works
On the Generalization of Deep Learning Models in Video Deepfake Detection
Coccomini, D. A., Caldelli, R., Falchi, F., & Gennaro, C. (2023) · Preprint (MDPI) · doi:10.20944/preprints202303.0161.v1
- Authors
- Nirkin, Y., Keller, Y., & Hassner, T.
- Affiliations
- Bar-Ilan University (IL)
- Open University of Israel (IL)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 7183-7192
- OpenAlex
- W2966880194
- Semantic Scholar
- ee4dba65e8d833272d62acf468e7cc880842811c
- Cited by
- 706
- References
- 57
- Citations by year
- 2026: 66 · 2025: 108 · 2024: 119 · 2023: 113 · 2022: 123 · 2021: 116 · 2020: 54 · 2019: 6 · 2018: 1
- FWCI
- 24.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- face swapping, face reenactment, recurrent neural networks, pose and expression variation, video interpolation, Delaunay triangulation, barycentric coordinates, face completion, perceptual loss
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.
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