Fetching the paper…
Reading the bibliography…
Trust in social media is a growing concern due to its ability to influence significant societal changes.
B. Kim, C. Rudin, and J. Shah, “The bayesian case model: a generative approach for case-based reasoning and prototype classification,” in Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 , ser. NIPS’14. Cambridge, MA, USA: MIT Press, 2014, p. 1952–1960
1960
Earlier work this paper cites.
H. V. Zhao, W. S. Lin, and K. R. Liu, “Behavior modeling and forensics for multimedia social networks,” IEEE Signal Processing Magazine , vol. 26, no. 1, pp. 118–139, 2009
2009
Earlier work this paper cites.
P. Garrido, L. Valgaerts, O. Rehmsen, T. Thormaehlen, P. Perez, and C. Theobalt, “Automatic face reenactment,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, June 2014. [Online]. Available: http://dx.doi.org/10.1109/CVPR.2014.537
2014
Earlier work this paper cites.
J. Thies, M. Zollhöfer, M. Nießner, L. Valgaerts, M. Stamminger, and C. Theobalt, “Real-time expression transfer for facial reenactment,” ACM Trans. Graph. , vol. 34, no. 6, nov 2015. [Online]. Available: https://doi.org/10.1145/2816795.2818056
2015
Earlier work this paper cites.
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE signal processing letters , vol. 23, no. 10, pp. 1499–1503, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
Y. Zhang, L. Zheng, and V. L. Thing, “Automated face swapping and its detection,” in 2017 IEEE 2nd international conference on signal and image processing (ICSIP) . IEEE, 2017, pp. 15–19
2017
Earlier work this paper cites.
P. Zhou, X. Han, V. I. Morariu, and L. S. Davis, “Two-stream neural networks for tampered face detection,” in 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW) . IEEE, 2017, pp. 1831–1839
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Alvarez-Melis and T. S. Jaakkola, “Towards robust interpretability with self-explaining neural networks,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems , ser. NIPS’18. Red Hook, NY, USA: Curran Associates Inc., 2018, p. 7786–7795
2018
Earlier work this paper cites.
Y. Li and S. Lyu, “Exposing deepfake videos by detecting face warping artifacts,” in CVPR Workshops , 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:53298495
2018
Earlier work this paper cites.
G. B. Meisner, The golden ratio: The divine beauty of mathematics . Race Point Publishing, 2018
2018
Earlier work this paper cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
Earlier work this paper cites.
D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen, “Mesonet: a compact facial video forgery detection network,” in 2018 IEEE international workshop on information forensics and security (WIFS) . IEEE, 2018, pp. 1–7
2018
Earlier work this paper cites.
D. Li, Y. Yang, Y.-Z. Song, and T. Hospedales, “Learning to generalize: Meta-learning for domain generalization,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
S. Fernandes, S. Raj, E. Ortiz, I. Vintila, M. Salter, G. Urosevic, and S. Jha, “Predicting heart rate variations of deepfake videos using neural ode,” in Proceedings of the IEEE/CVF international conference on computer vision workshops , 2019, pp. 0–0
2019
Earlier work this paper cites.
E. Sabir, J. Cheng, A. Jaiswal, W. AbdAlmageed, I. Masi, and P. Natarajan, “Recurrent convolutional strategies for face manipulation detection in videos,” Interfaces (GUI) , vol. 3, no. 1, pp. 80–87, 2019
2019
Earlier work this paper cites.
A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “Faceforensics++: Learning to detect manipulated facial images,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 1–11
2019
Earlier work this paper cites.
X. Yang, Y. Li, and S. Lyu, “Exposing deep fakes using inconsistent head poses,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 8261–8265
2019
Earlier work this paper cites.
F. Matern, C. Riess, and M. Stamminger, “Exploiting visual artifacts to expose deepfakes and face manipulations,” in 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) . IEEE, 2019, pp. 83–92
2019
Earlier work this paper cites.
S. Agarwal, H. Farid, Y. Gu, M. He, K. Nagano, and H. Li, “Protecting world leaders against deep fakes.” in CVPR workshops , vol. 1, 2019, p. 38
2019
Earlier work this paper cites.
S. McCloskey and M. Albright, “Detecting gan-generated imagery using saturation cues,” in 2019 IEEE international conference on image processing (ICIP) . IEEE, 2019, pp. 4584–4588
2019
Earlier work this paper cites.
C. Chen, O. Li, C. Tao, A. J. Barnett, J. Su, and C. Rudin, This looks like that: deep learning for interpretable image recognition . Red Hook, NY, USA: Curran Associates Inc., 2019
2019
Cited alongside, same era.
H. H. Nguyen, F. Fang, J. Yamagishi, and I. Echizen, “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) . IEEE, 2019, pp. 1–8
2019
Cited alongside, same era.
S. Tariq, S. Lee, H. Kim, Y. Shin, and S. S. Woo, “Gan is a friend or foe? a framework to detect various fake face images,” in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing , 2019, pp. 1296–1303
2019
Cited alongside, same era.
L. Li, J. Bao, T. Zhang, H. Yang, D. Chen, F. Wen, and B. Guo, “Face x-ray for more general face forgery detection,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 5000–5009, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:209516424
Y. Luo, Y. Zhang, J. Yan, and W. Liu, “Generalizing face forgery detection with high-frequency features,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 16 317–16 326
2021
Later among the works it cites.
K. Sun, H. Liu, Q. Ye, Y. Gao, J. Liu, L. Shao, and R. Ji, “Domain general face forgery detection by learning to weight,” in Proceedings of the AAAI conference on artificial intelligence , vol. 35, no. 3, 2021, pp. 2638–2646
2021
Later among the works it cites.
S. Chen, T. Yao, Y. Chen, S. Ding, J. Li, and R. Ji, “Local relation learning for face forgery detection,” in Proceedings of the AAAI conference on artificial intelligence , vol. 35, no. 2, 2021, pp. 1081–1088
2021
Later among the works it cites.
S. Xiao, G. Lan, J. Yang, Y. Li, and J. Wen, “Securing the socio-cyber world: multiorder attribute node association classification for manipulated media,” IEEE Transactions on Computational Social Systems , no. 99, pp. 1–10, 2022
2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
N. Dufour and A. Gully, “Contributing data to deepfake detection research,” https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html , 2019
2019
Cited alongside, same era.
A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “Faceforensics++: Learning to detect manipulated facial images,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 1–11
2019
Cited alongside, same era.
M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning . PMLR, 2019, pp. 6105–6114
2019
Cited alongside, same era.
T. Jung, S. Kim, and K. Kim, “Deepvision: Deepfakes detection using human eye blinking pattern,” IEEE Access , vol. 8, pp. 83 144–83 154, 2020
2020
Cited alongside, same era.
S. Agarwal, H. Farid, T. El-Gaaly, and S.-N. Lim, “Detecting deep-fake videos from appearance and behavior,” in 2020 IEEE international workshop on information forensics and security (WIFS) . IEEE, 2020, pp. 1–6
2020
Cited alongside, same era.
H. Qi, Q. Guo, F. Juefei-Xu, X. Xie, L. Ma, W. Feng, Y. Liu, and J. Zhao, “Deeprhythm: Exposing deepfakes with attentional visual heartbeat rhythms,” in Proceedings of the 28th ACM international conference on multimedia , 2020, pp. 4318–4327
2020
Cited alongside, same era.
Z. Zhao, P. Wang, and W. Lu, “Detecting deepfake video by learning two-level features with two-stream convolutional neural network,” in Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence , 2020, pp. 291–297
2020
Cited alongside, same era.
A. Haliassos, K. Vougioukas, S. Petridis, and M. Pantic, “Lips don’t lie: A generalisable and robust approach to face forgery detection,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 5037–5047, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:229156239
2020
Cited alongside, same era.
Later among the works it cites.
M. S. Saealal, M. Z. Ibrahim, D. J. Mulvaney, M. I. Shapiai, and N. Fadilah, “Using cascade cnn-lstm-fcns to identify ai-altered video based on eye state sequence,” PLoS One , vol. 17, no. 12, p. e0278989, 2022
2022
Later among the works it cites.
T. Menzel, M. Botsch, and M. E. Latoschik, “Automated blendshape personalization for faithful face animations using commodity smartphones,” in Proceedings of the 28th ACM Symposium on Virtual Reality Software and Technology , 2022, pp. 1–9
2022
Later among the works it cites.
M. Kim, A. K. Jain, and X. Liu, “Adaface: Quality adaptive margin for face recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 18 750–18 759
2022
Later among the works it cites.
L. Chen, Y. Zhang, Y. Song, L. Liu, and J. Wang, “Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 18 710–18 719
2022
Later among the works it cites.
K. Shiohara and T. Yamasaki, “Detecting deepfakes with self-blended images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 720–18 729
2022
Later among the works it cites.
K. Sun, T. Yao, S. Chen, S. Ding, J. Li, and R. Ji, “Dual contrastive learning for general face forgery detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 2, 2022, pp. 2316–2324
2022
Later among the works it cites.
W. Zhuang, Q. Chu, Z. Tan, Q. Liu, H. Yuan, C. Miao, Z. Luo, and N. Yu, “Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection,” in European conference on computer vision . Springer, 2022, pp. 391–407
2022
Later among the works it cites.
S. Lee and S. Hong, “D-tsm: Discriminative temporal shift module for action recognition,” in 2023 20th International Conference on Ubiquitous Robots (UR) . IEEE, 2023, pp. 133–136
2023
Later among the works it cites.
W. Potok, A. Post, V. Beliaeva, M. Bächinger, A. M. Cassarà, E. Neufeld, R. Polania, D. Kiper, and N. Wenderoth, “Modulation of visual contrast sensitivity with trns across the visual system, evidence from stimulation and simulation,” eneuro , vol. 10, no. 6, 2023
2023
Later among the works it cites.
M. A. Raza, K. M. Malik, and I. U. Haq, “Holisticdfd: Infusing spatiotemporal transformer embeddings for deepfake detection,” Information Sciences , vol. 645, p. 119352, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
B. Huang, Z. Wang, J. Yang, J. Ai, Q. Zou, Q. Wang, and D. Ye, “Implicit identity driven deepfake face swapping detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4490–4499
2023
Later among the works it cites.
U. A. Çiftçi, İ. Demir, and L. Yin, “Deepfake source detection in a heart beat,” The Visual Computer , vol. 40, no. 4, pp. 2733–2750, 2024
2024
Closest in time.
A. L. Pellicer, Y. Li, and P. Angelov, “Pudd: Towards robust multi-modal prototype-based deepfake detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 3809–3817
2024
Closest in time.
A. Agarwal and N. Ratha, “Deepfake catcher: Can a simple fusion be effective and outperform complex dnns?” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 3791–3801
2024
Closest in time.
Z. Yan, Y. Luo, S. Lyu, Q. Liu, and B. Wu, “Transcending forgery specificity with latent space augmentation for generalizable deepfake detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 8984–8994
2024
Closest in time.
2024
Closest in time.
Google, “Mediapipe face landmarker,” https://storage.googleapis.com/mediapipe-assets/Model%20Card%20MediaPipe%20Face%20Mesh%20V2.pdf , 2024
2024
Closest in time.