Fetching the paper…
Reading the bibliography…
With the powerful deep network architectures, such as generative adversarial networks, one can easily generate photorealistic images.
R. M. Haralick, K. Shanmugam, I. Dinstein, Textural features for image classification, IEEE Trans. Syst., Man, Cybern. SMC-3 (6) (1973) 610–621
1973
Earlier work this paper cites.
G. B. Huang, M. Ramesh, T. Berg, E. Learned-Miller, Labeled Faces in the Wild: A database for studying face recognition in unconstrained environments, Tech. Rep. 07-49, University of Massachusetts, Amherst (Oct. 2007)
2007
Earlier work this paper cites.
W. Luo, J. Huang, G. Qiu, JPEG error analysis and its applications to digital image forensics, IEEE Trans. Inf. Forensics Security 5 (3) (2010) 480–491
2010
Earlier work this paper cites.
2011
Earlier work this paper cites.
T. Bianchi, A. Piva, Image forgery localization via block-grained analysis of JPEG artifacts, IEEE Trans. Inf. Forensics Security 7 (3) (2012) 1003–1017
2012
Earlier work this paper cites.
J. Fridrich, J. Kodovsky, Rich models for steganalysis of digital images, IEEE Trans. Inf. Forensics Security 7 (3) (2012) 868–882
2012
Earlier work this paper cites.
J. Kodovsky, J. Fridrich, V. Holub, Ensemble classifiers for steganalysis of digital media, IEEE Trans. Inf. Forensics Security 7 (2) (2012) 432–444
2012
Earlier work this paper cites.
M. C. Stamm, X. Chu, K. R. Liu, Forensically determining the order of signal processing operations, in: Proc. IEEE Int. Workshop Information Forensics and Security (WIFS), 2013, pp. 162–167
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Proc. Conf. Neural Information Processing Systems (NeurIPS), 2014, pp. 2672–2680
2014
Earlier work this paper cites.
D. P. Kingma, M. Welling, Auto-encoding variational Bayes, in: Proc. Int. Conf. Learning Representations (ICLR), 2014
2014
Earlier work this paper cites.
J. Galbally, S. Marcel, J. Fierrez, Biometric antispoofing methods: A survey in face recognition, IEEE Access 2 (2014) 1530–1552
2014
Earlier work this paper cites.
J. Galbally, S. Marcel, Face anti-spoofing based on general image quality assessment, in: Proc. Int. Conf. Pattern Recognition (ICPR), 2014, pp. 1173–1178
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
D. Wen, H. Han, A. K. Jain, Face spoof detection with image distortion analysis, IEEE Trans. Inf. Forensics Security 10 (4) (2015) 746–761
2015
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, X. Tang, Deep learning face attributes in the wild, in: Proc. IEEE Int. Conf. Computer Vision (ICCV), 2015, pp. 3730–3738
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. van den Oord, N. Kalchbrenner, K. Kavukcuoglu, Pixel recurrent neural networks, in: Proc. Int. Conf. Machine Learning (ICML), 2016, pp. 1747–1756
2016
Earlier work this paper cites.
P. Korus, J. Huang, Multi-scale fusion for improved localization of malicious tampering in digital images, IEEE Trans. Image Process. 25 (3) (2016) 1312–1326
2016
Earlier work this paper cites.
K. Patel, H. Han, A. K. Jain, Secure face unlock: Spoof detection on smartphones, IEEE Trans. Inf. Forensics Security 11 (10) (2016) 2268–2283
2016
Earlier work this paper cites.
Z. Boulkenafet, J. Komulainen, A. Hadid, Face spoofing detection using colour texture analysis, IEEE Trans. Inf. Forensics Security 11 (8) (2016) 1818–1830
2016
Earlier work this paper cites.
A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, in: Proc. Int. Conf. Learning Representations (ICLR), 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, J. Sun, Identity mappings in deep residual networks, in: Proc. European Conf. on Computer Vision (ECCV), 2016, pp. 630–645
2016
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al., Photo-realistic single image super-resolution using a generative adversarial network, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4681–4690
2017
Cited alongside, same era.
J.-Y. Zhu, T. Park, P. Isola, A. A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proc. IEEE Int. Conf. Computer Vision (ICCV), 2017, pp. 2223–2232
2017
Cited alongside, same era.
M.-Y. Liu, T. Breuel, J. Kautz, Unsupervised image-to-image translation networks, in: Proc. Conf. Neural Information Processing Systems (NeurIPS), 2017, pp. 700–708
2017
Cited alongside, same era.
B. Bayar, M. C. Stamm, Constrained convolutional neural networks: A new approach towards general purpose image manipulation detection, IEEE Trans. Inf. Forensics Security 13 (11) (2018) 2691–2706
2018
Closest in time.
Y. Li, M.-C. Chang, S. Lyu, In ictu oculi: Exposing AI created fake videos by detecting eye blinking, in: Proc. IEEE Int. Workshop Information Forensics and Security (WIFS), 2018, pp. 1–7
2018
Closest in time.
D. Afchar, V. Nozick, J. Yamagishi, I. Echizen, Mesonet: a compact facial video forgery detection network, in: Proc. IEEE Int. Workshop Information Forensics and Security (WIFS), 2018, pp. 1–7
2018
Closest in time.
2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Iizuka, E. Simo-Serra, H. Ishikawa, Globally and locally consistent image completion, ACM Trans. Graphics 36 (4) (2017) 107:1–107:14
2017
Cited alongside, same era.
J. Snow, AI could set us back 100 years when it comes to how we consume news, available: https://www.technologyreview.com/s/609358 (Nov. 2017)
2017
Cited alongside, same era.
P. Korus, Digital image integrity–a survey of protection and verification techniques, Digital Signal Processing 71 (2017) 1–26
2017
Cited alongside, same era.
Z. Chen, Y. Zhao, R. Ni, Detection of operation chain: JPEG-resampling-JPEG, Signal Processing: Image Communication 57 (2017) 8–20
2017
Cited alongside, same era.
H. Li, W. Luo, X. Qiu, J. Huang, Image forgery localization via integrating tampering possibility maps, IEEE Trans. Inf. Forensics Security 12 (5) (2017) 1240–1252
2017
Cited alongside, same era.
X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, S. P. Smolley, Least squares generative adversarial networks, in: Proc. IEEE Int. Conf. Computer Vision (ICCV), 2017, pp. 2813–2821
2017
Cited alongside, same era.
J. Zhao, M. Mathieu, Y. LeCun, Energy-based generative adversarial network, in: Proc. Int. Conf. Learning Representations (ICLR), 2017
2017
Cited alongside, same era.
M. Arjovsky, S. Chintala, L. Bottou, Wasserstein GAN, arXiv preprint arXiv:1701.07875 (2017)
2017
Cited alongside, same era.
Z. Wang, B. Chen, H. Zhang, H. Liu, Variational probabilistic generative framework for single image super-resolution, Signal Processing 156 (2019) 92–105
2019
Closest in time.
Virginia bans ‘deepfakes’ and ‘deepnudes’ pornography, available: https://www.bbc.com/news/technology-48839758 (Jul. 2019)
2019
Closest in time.
Q. Zhang, W. Lu, T. Huang, S. Luo, Z. Xu, Y. Mao, On the robustness of JPEG post-compression to resampling factor estimation, Signal Processing (2019) 107371
2019
Closest in time.
F. Matern, C. Riess, M. Stamminger, Exploiting visual artifacts to expose deepfakes and face manipulations, in: Proc. IEEE Winter Applications of Computer Vision Workshops (WACVW), 2019, pp. 83–92
2019
Closest in time.
S. McCloskey, M. Albright, Detecting GAN-generated imagery using saturation cues, in: Proc. IEEE Int. Conf. Image Processing (ICIP), 2019, pp. 4584–4588
2019
Closest in time.
Y.-X. Zhuang, C.-C. Hsu, Detecting generated image based on a coupled network with two-step pairwise learning, in: Proc. IEEE Int. Conf. Image Processing (ICIP), 2019, pp. 3212–3216
2019
Closest in time.
C. H. Lin, C.-C. Chang, Y.-S. Chen, D.-C. Juan, W. Wei, H.-T. Chen, COCO-GAN: Generation by parts via conditional coordinating, in: Proc. IEEE Int. Conf. Computer Vision (ICCV), 2019, pp. 4512–4521
2019
Closest in time.
A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, M. Nießner, Faceforensics++: Learning to detect manipulated facial images, in: Proc. IEEE Int. Conf. Computer Vision (ICCV), 2019, pp. 1–11
2019
Closest in time.
F. Marra, D. Gragnaniello, L. Verdoliva, G. Poggi, Do GANs leave artificial fingerprints?, in: Proc. IEEE Conf. Multimedia Information Processing and Retrieval (MIPR), 2019, pp. 506–511
2019
Closest in time.
X. Yang, Y. Li, S. Lyu, Exposing deep fakes using inconsistent head poses, in: Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 8261–8265
2019
Closest in time.
X. Yang, Y. Li, H. Qi, S. Lyu, Exposing GAN-synthesized faces using landmark locations, in: Proc. ACM Workshop Information Hiding and Multimedia Security, 2019, pp. 113–118
2019
Closest in time.
S. Agarwal, H. Farid, Y. Gu, M. He, K. Nagano, H. Li, Protecting world leaders against deep fakes, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR) Workshops, 2019
2019
Closest in time.
L. Nataraj, T. M. Mohammed, B. Manjunath, S. Chandrasekaran, A. Flenner, J. H. Bappy, A. K. Roy-Chowdhury, Detecting GAN generated fake images using co-occurrence matrices, Electronic Imaging 2019 (5) (2019) 532–1–532–7
2019
Closest in time.
Y. Li, S. Lyu, Exposing deepfake videos by detecting face warping artifacts, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR) Workshops, 2019
2019
Closest in time.
X. Zhang, S. Karaman, S.-F. Chang, Detecting and simulating artifacts in GAN fake images, in: Proc. IEEE Int. Workshop Information Forensics and Security (WIFS), 2019
2019
Closest in time.