Fetching the paper…
Reading the bibliography…
Recently, AI-manipulated face techniques have developed rapidly and constantly, which has raised new security issues in society.
B. K. Gunturk, Y. Altunbasak, and R. M. Mersereau, “Color plane interpolation using alternating projections,” IEEE transactions on image processing , vol. 11, no. 9, pp. 997–1013, 2002
2002
Earlier work this paper cites.
P.-T. De Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, “A tutorial on the cross-entropy method,” Annals of operations research , vol. 134, no. 1, pp. 19–67, 2005
2005
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” The Journal of Machine Learning Research , vol. 13, no. 1, pp. 723–773, 2012
2012
Earlier work this paper cites.
X. Zhao, S. Wang, S. Li, and J. Li, “Passive image-splicing detection by a 2-d noncausal markov model,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 25, no. 2, pp. 185–199, 2014
2014
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in neural information processing systems , 2014, pp. 3320–3328
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
S. Chen, S. Tan, B. Li, and J. Huang, “Automatic detection of object-based forgery in advanced video,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 26, no. 11, pp. 2138–2151, 2015
2015
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 3730–3738
2015
Earlier work this paper cites.
J. Thies, M. Zollhofer, M. Stamminger, C. Theobalt, and M. Nießner, “Face2face: Real-time face capture and reenactment of rgb videos,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2387–2395
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and S. Jian, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision & Pattern Recognition , 2016
2016
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.
I. Korshunova, W. Shi, J. Dambre, and L. Theis, “Fast face-swap using convolutional neural networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 3677–3685
2017
Earlier work this paper cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1251–1258
2017
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of machine learning research , vol. 9, no. Nov, pp. 2579–2605, 2008
2017
Earlier work this paper cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in International Conference on Learning Representations , 2018
2018
Earlier work this paper cites.
H. Ding, K. Sricharan, and R. Chellappa, “Exprgan: Facial expression editing with controllable expression intensity,” in Thirty-second AAAI conference on artificial intelligence , 2018
2018
Earlier work this paper cites.
A. Pumarola, A. Agudo, A. M. Martinez, A. Sanfeliu, and F. Moreno-Noguer, “Ganimation: Anatomically-aware facial animation from a single image,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 818–833
2018
Earlier work this paper cites.
Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8789–8797
2018
Earlier work this paper cites.
Y. Li, M.-C. Chang, and S. Lyu, “In ictu oculi: Exposing ai created fake videos by detecting eye blinking,” in 2018 IEEE International Workshop on Information Forensics and Security (WIFS) . IEEE, 2018, pp. 1–7
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
Cited alongside, same era.
D. Güera and E. J. Delp, “Deepfake video detection using recurrent neural networks,” in 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) . IEEE, 2018, pp. 1–6
2018
Cited alongside, same era.
2018
Cited alongside, same era.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2019, pp. 4401–4410
2019
Cited alongside, same era.
Y. Chen, H. Fan, B. Xu, Z. Yan, Y. Kalantidis, M. Rohrbach, S. Yan, and J. Feng, “Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution,” arXiv: Computer Vision and Pattern Recognition , 2019
2019
Later among the works it cites.
Y. Li and S. Lyu, “Exposing deepfake videos by detecting face warping artifacts,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2019, pp. 46–52
2019
Later among the works it cites.
X. Yang, Y. Li, H. Qi, and S. Lyu, “Exposing gan-synthesized faces using landmark locations,” in Proceedings of the ACM Workshop on Information Hiding and Multimedia Security , 2019, pp. 113–118
2019
Later among the works it 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
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y.-C. Chen, X. Xu, Z. Tian, and J. Jia, “Homomorphic latent space interpolation for unpaired image-to-image translation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2408–2416
2019
Cited alongside, same era.
Z. He, W. Zuo, M. Kan, S. Shan, and X. Chen, “Attgan: Facial attribute editing by only changing what you want,” IEEE Transactions on Image Processing , vol. 28, no. 11, pp. 5464–5478, 2019
2019
Cited alongside, same era.
M. Liu, Y. Ding, M. Xia, X. Liu, E. Ding, W. Zuo, and S. Wen, “Stgan: A unified selective transfer network for arbitrary image attribute editing,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2019, pp. 3673–3682
2019
Cited alongside, same era.
J. Thies, M. Zollhöfer, and M. Nießner, “Deferred neural rendering: Image synthesis using neural textures,” ACM Transactions on Graphics (TOG) , vol. 38, no. 4, pp. 1–12, 2019
2019
Cited alongside, same era.
L. M. Dang, S. I. Hassan, S. Im, and H. Moon, “Face image manipulation detection based on a convolutional neural network,” Expert Systems with Applications , vol. 129, pp. 156–168, 2019
2019
Cited alongside, same era.
Y. Liu, X. Zhu, X. Zhao, and Y. Cao, “Adversarial learning for constrained image splicing detection and localization based on atrous convolution,” IEEE Transactions on Information Forensics and Security , vol. 14, no. 10, pp. 2551–2566, 2019
2019
Cited alongside, same era.
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
Cited alongside, same era.
N. Yu, L. S. Davis, and M. Fritz, “Attributing fake images to gans: Learning and analyzing gan fingerprints,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 7556–7566
2019
Cited alongside, same era.
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 International Conference on Computer Vision Workshops , 2019, pp. 0–0
2019
Later among the works it 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
Later among the works it cites.
P. He, H. Li, and H. Wang, “Detection of fake images via the ensemble of deep representations from multi color spaces,” in 2019 IEEE International Conference on Image Processing (ICIP) . IEEE, 2019, pp. 2299–2303
2019
Later among the works it cites.
X. Xuan, B. Peng, W. Wang, and J. Dong, “On the generalization of gan image forensics,” in Chinese Conference on Biometric Recognition . Springer, 2019, pp. 134–141
2019
Later among the works it cites.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8110–8119
2020
Closest in time.
2020
Closest in time.
S.-Y. Wang, O. Wang, R. Zhang, A. Owens, and A. A. Efros, “Cnn-generated images are surprisingly easy to spot… for now,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , vol. 7, 2020
2020
Closest in time.
H. Dang, F. Liu, J. Stehouwer, X. Liu, and A. K. Jain, “On the detection of digital face manipulation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 5781–5790
2020
Closest in time.
L. Li, J. Bao, T. Zhang, H. Yang, D. Chen, F. Wen, and B. Guo, “Face x-ray for more general face forgery detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 5001–5010
2020
Closest in time.
Z. Mi, X. Jiang, T. Sun, and K. Xu, “Gan-generated image detection with self-attention mechanism against gan generator defect,” IEEE Journal of Selected Topics in Signal Processing , 2020
2020
Closest in time.
Z. Liu, X. Qi, and P. H. Torr, “Global texture enhancement for fake face detection in the wild,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8060–8069
2020
Closest in time.
2020
Closest in time.
H. Li, B. Li, S. Tan, and J. Huang, “Identification of deep network generated images using disparities in color components,” Signal Processing , p. 107616, 2020
2020
Closest in time.