Fetching the paper…
Reading the bibliography…
The current high-fidelity generation and high-precision detection of DeepFake images are at an arms race.
R. C. Gonzales and R. E. Woods, “Digital image processing,” 2002
2002
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising with block-matching and 3d filtering,” in Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning , vol. 6064. International Society for Optics and Photonics, 2006, p. 606414
2006
Earlier work this paper cites.
K. He, J. Sun, and X. Tang, “Guided image filtering,” in European conference on computer vision . Springer, 2010, pp. 1–14
2010
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
B. Shi, X. Bai, and C. Yao, “An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 11, pp. 2298–2304, 2016
2016
Earlier work this paper cites.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” 2017
2017
Earlier work this paper cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” Advances in neural information processing systems , vol. 30, pp. 5767–5777, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C.-L. Li, W.-C. Chang, Y. Cheng, Y. Yang, and B. Póczos, “Mmd gan: Towards deeper understanding of moment matching network,” in Advances in Neural Information Processing Systems , 2017, pp. 2203–2213
2017
Earlier work this paper cites.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2223–2232
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
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.
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.
R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” in Advances in neural information processing systems , 2018, pp. 6571–6583
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.
C. Chen, Q. Chen, J. Xu, and V. Koltun, “Learning to see in the dark,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3291–3300
2018
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Large-scale celebfaces attributes (celeba) dataset,” Retrieved August , vol. 15, p. 2018, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
K. Li and J. Malik, “Implicit maximum likelihood estimation,” arXiv preprint arXiv:1809.09087 , 2018
2018
Earlier work this paper cites.
B. Mildenhall, J. T. Barron, J. Chen, D. Sharlet, R. Ng, and R. Carroll, “Burst denoising with kernel prediction networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2502–2510
2018
Earlier work this paper cites.
X. Zhang, S. Karaman, and S.-F. Chang, “Detecting and simulating artifacts in gan fake images,” in 2019 IEEE International Workshop on Information Forensics and Security (WIFS) . IEEE, 2019, pp. 1–6
2019
Earlier work this paper cites.
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
Earlier work this paper cites.
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.
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 , 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.
T. Dai, J. Cai, Y. Zhang, S.-T. Xia, and L. Zhang, “Second-order attention network for single image super-resolution,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 065–11 074
2019
Cited alongside, same era.
K. Chugh, P. Gupta, A. Dhall, and R. Subramanian, “Not made for each other-audio-visual dissonance-based deepfake detection and localization,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 439–447
2020
Closest in time.
S. Agarwal, H. Farid, O. Fried, and M. Agrawala, “Detecting deep-fake videos from phoneme-viseme mismatches,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 660–661
2020
Closest in time.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Closest in time.
Y. Huang, F. Juefei-Xu, R. Wang, Q. Guo, L. Ma, X. Xie, J. Li, W. Miao, Y. Liu, and G. Pu, “Fakepolisher: Making deepfakes more detection-evasive by shallow reconstruction,” in Proceedings of the 28th ACM international conference on multimedia , 2020, pp. 1217–1226
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. H. Nguyen, J. Yamagishi, and I. Echizen, “Capsule-forensics: Using capsule networks to detect forged images and videos,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 2307–2311
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. 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
Cited alongside, same era.
H. R. Hasan and K. Salah, “Combating deepfake videos using blockchain and smart contracts,” Ieee Access , vol. 7, pp. 41 596–41 606, 2019
2019
Cited alongside, same era.
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
Cited alongside, same era.
F. Marra, D. Gragnaniello, L. Verdoliva, and G. Poggi, “Do gans leave artificial fingerprints?” in 2019 IEEE conference on multimedia information processing and retrieval (MIPR) . IEEE, 2019, pp. 506–511
2019
Cited alongside, same era.
N. Rahaman, A. Baratin, D. Arpit, F. Draxler, M. Lin, F. Hamprecht, Y. Bengio, and A. Courville, “On the spectral bias of neural networks,” in International Conference on Machine Learning . PMLR, 2019, pp. 5301–5310
2019
Cited alongside, same era.
T. Park, M.-Y. Liu, T.-C. Wang, and J.-Y. Zhu, “Gaugan: semantic image synthesis with spatially adaptive normalization,” in ACM SIGGRAPH 2019 Real-Time Live! ACM, 2019, pp. 1–1
2019
Cited alongside, same era.
J. Deng, L. Wang, S. Pu, and C. Zhuo, “Spatio-temporal deformable convolution for compressed video quality enhancement,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 07, 2020, pp. 10 696–10 703
2020
Closest in time.
J. Li, H. Xie, J. Li, Z. Wang, and Y. Zhang, “Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 6458–6467
2021
Closest in time.
H. Liu, X. Li, W. Zhou, Y. Chen, Y. He, H. Xue, W. Zhang, and N. Yu, “Spatial-phase shallow learning: rethinking face forgery detection in frequency domain,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 772–781
2021
Closest in time.
H. Zhao, W. Zhou, D. Chen, T. Wei, W. Zhang, and N. Yu, “Multi-attentional deepfake detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 2185–2194
2021
Closest in time.
Y. Gao, F. Wei, J. Bao, S. Gu, D. Chen, F. Wen, and Z. Lian, “High-fidelity and arbitrary face editing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 16 115–16 124
2021
Closest in time.
2021
Closest in time.
J. Hu, X. Liao, W. Wang, and Z. Qin, “Detecting compressed deepfake videos in social networks using frame-temporality two-stream convolutional network,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 3, pp. 1089–1102, 2021
2021
Closest in time.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=PxTIG12RRHS
2021
Closest in time.
X. Shu, X. Wang, X. Zang, S. Zhang, Y. Chen, G. Li, and Q. Tian, “Large-scale spatio-temporal person re-identification: Algorithms and benchmark,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 7, pp. 4390–4403, 2021
2021
Closest in time.
B. Chen, L. Zhu, G. Li, F. Lu, H. Fan, and S. Wang, “Learning generalized spatial-temporal deep feature representation for no-reference video quality assessment,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 4, pp. 1903–1916, 2021
2021
Closest in time.
Y. Ni, D. Meng, C. Yu, C. Quan, D. Ren, and Y. Zhao, “Core: Consistent representation learning for face forgery detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12–21
2022
Closest in time.
C. Dong, A. Kumar, and E. Liu, “Think twice before detecting gan-generated fake images from their spectral domain imprints,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 7865–7874
2022
Closest in time.
Y. Jeong, D. Kim, Y. Ro, and J. Choi, “Frepgan: robust deepfake detection using frequency-level perturbations,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 1, 2022, pp. 1060–1068
2022
Closest in time.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 10 684–10 695
2022
Closest in time.
C. Meng, Y. He, Y. Song, J. Song, J. Wu, J.-Y. Zhu, and S. Ermon, “SDEdit: Guided image synthesis and editing with stochastic differential equations,” in International Conference on Learning Representations , 2022
2022
Closest in time.
J. Liu, C. Li, Y. Ren, F. Chen, and Z. Zhao, “Diffsinger: Singing voice synthesis via shallow diffusion mechanism,” in Proceedings of the AAAI conference on artificial intelligence , vol. 36, no. 10, 2022, pp. 11 020–11 028
2022
Closest in time.
W. Nie, B. Guo, Y. Huang, C. Xiao, A. Vahdat, and A. Anandkumar, “Diffusion models for adversarial purification,” in International Conference on Machine Learning (ICML) , 2022
2022
Closest in time.
C. Chen, M. Ye, M. Qi, J. Wu, Y. Liu, and J. Jiang, “Saliency and granularity: Discovering temporal coherence for video-based person re-identification,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 9, pp. 6100–6112, 2022
2022
Closest in time.
M. Liu, S. Jin, C. Yao, C. Lin, and Y. Zhao, “Temporal consistency learning of inter-frames for video super-resolution,” IEEE Transactions on Circuits and Systems for Video Technology , 2022
2022
Closest in time.
2022
Closest in time.
Stability AI, “Stable diffusion version 2,” 2023, accessed: 2023-05-01. [Online]. Available: https://github.com/Stability-AI/stablediffusion
2023
Closest in time.
C. Liu, H. Chen, T. Zhu, J. Zhang, and W. Zhou, “Making deepfakes more spurious: evading deep face forgery detection via trace removal attack,” IEEE Transactions on Dependable and Secure Computing , 2023
2023
Closest in time.
Y. Hou, Q. Guo, Y. Huang, X. Xie, L. Ma, and J. Zhao, “Evading deepfake detectors via adversarial statistical consistency,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 12 271–12 280
2023
Closest in time.