Fetching the paper…
Reading the bibliography…
Detecting manipulated images and videos is an important topic in digital media forensics.
The digital emily project: Achieving a photorealistic digital actor
O. Alexander, M. Rogers, W. Lambeth, J.-Y. Chiang, W.-C. Ma, C.-C. Wang, and P. Debevec · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
Rich models for steganalysis of digital images
J. Fridrich and J. Kodovsky · 2012
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Earlier work this paper cites.
A deep learning approach to universal image manipulation detection using a new convolutional layer
B. Bayar and M. C. Stamm · 2016
Earlier work this paper cites.
Face2Face: Real-time face capture and reenactment of RGB videos
J. Thies, M. Zollhofer, M. Stamminger, C. Theobalt, and M. Nießner · 2016
Earlier work this paper cites.
Bringing portraits to life
H. Averbuch-Elor, D. Cohen-Or, J. Kopf, and M. F. Cohen · 2017
Earlier work this paper cites.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2017
Earlier work this paper cites.
Exploiting spatial structure for localizing manipulated image regions
J. H. Bappy, A. K. Roy-Chowdhury, J. Bunk, L. Nataraj, and B. Manjunath · 2017
Cited alongside, same era.
You said that?
J. S. Chung, A. Jamaludin, and A. Zisserman · 2017
Cited alongside, same era.
Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection
D. Cozzolino, G. Poggi, and L. Verdoliva · 2017
Cited alongside, same era.
Transferable deep-CNN features for detecting digital and print-scanned morphed face images
R. Raghavendra, K. B. Raja, S. Venkatesh, and C. Busch · 2017
Cited alongside, same era.
Distinguishing computer graphics from natural images using convolution neural networks
N. Rahmouni, V. Nozick, J. Yamagishi, and I. Echizen · 2017
Cited alongside, same era.
Synthesizing obama: learning lip sync from audio
In ictu oculi: Exposing ai created fake videos by detecting eye blinking
Y. Li, M.-C. Chang, and S. Lyu · 2018
Later among the works it cites.
The voice conversion challenge 2018: Promoting development of parallel and nonparallel methods
J. Lorenzo-Trueba, J. Yamagishi, T. Toda, D. Saito, F. Villavicencio, T. Kinnunen, and Z. Ling · 2018
Later among the works it cites.
Modular convolutional neural network for discriminating between computer-generated images and photographic images
H. H. Nguyen, T. Tieu, H.-Q. Nguyen-Son, V. Nozick, J. Yamagishi, and I. Echizen · 2018
Later among the works it cites.
Distinguishing between natural and computer-generated images using convolutional neural networks
W. Quan, K. Wang, D.-M. Yan, and X. Zhang · 2018
Later among the works it cites.
Faceforensics: A large-scale video dataset for forgery detection in human faces
A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Suwajanakorn, S. M. Seitz, and I. Kemelmacher-Shlizerman · 2017
Cited alongside, same era.
Two-stream neural networks for tampered face detection
P. Zhou, X. Han, V. I. Morariu, and L. S. Davis · 2017
Cited alongside, same era.
MesoNet: a compact facial video forgery detection network
D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen · 2018
Cited alongside, same era.
Forensictransfer: Weakly-supervised domain adaptation for forgery detection
D. Cozzolino, J. Thies, A. Rössler, C. Riess, M. Nießner, and L. Verdoliva · 2018
Cited alongside, same era.
Deep video portraits
H. Kim, P. Garrido, A. Tewari, W. Xu, J. Thies, M. Nießner, P. Pérez, C. Richardt, M. Zollhöfer, and C. Theobalt · 2018
Cited alongside, same era.
Deepfakes: a new threat to face recognition? assessment and detection
P. Korshunov and S. Marcel · 2018
Cited alongside, same era.
Later among the works it cites.
Learning rich features for image manipulation detection
P. Zhou, X. Han, V. I. Morariu, and L. S. Davis · 2018
Later among the works it cites.
https://www.foxnews.com/tech/terrifying-high-tech-porn-creepy-deepfake-videos-are-on-the-rise
Terrifying high-tech porn: Creepy ’deepfake’ videos are on the rise · 2019
Closest in time.
Hybrid lstm and encoder-decoder architecture for detection of image forgeries
J. H. Bappy, C. Simons, L. Nataraj, B. Manjunath, and A. K. Roy-Chowdhury · 2019
Closest in time.
Capsule-forensics: Using capsule networks to detect forged images and videos
H. H. Nguyen, J. Yamagishi, and I. Echizen · 2019
Closest in time.
Faceforensics++: Learning to detect manipulated facial images
A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner · 2019
Closest in time.