Fetching the paper…
Reading the bibliography…
Recent technological advances in synthetic data have enabled the generation of images with such high quality that human beings cannot tell the difference between real-life photographs and Artificial Intelligence (AI) generated images.
A. Krizhevsky, G. Hinton, et al
2009
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature
2015
Earlier work this paper cites.
Software available from tensorflow.org
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015 · 2015
Earlier work this paper cites.
S. J. Nightingale, K. A. Wade, and D. G. Watson, “Can people identify original and manipulated photos of real-world scenes?,” Cognitive research: principles and implications
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision
2017
Earlier work this paper cites.
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)
2018
Earlier work this paper cites.
J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, et al
2018
Earlier work this paper cites.
I. Amerini, L. Galteri, R. Caldelli, and A. Del Bimbo, “Deepfake video detection through optical flow based cnn,” in Proceedings of the IEEE/CVF international conference on computer vision workshops
2019
Earlier work this paper cites.
D. Gunning, M. Stefik, J. Choi, T. Miller, S. Stumpf, and G.-Z. Yang, “Xai—explainable artificial intelligence,” Science robotics
2019
Earlier work this paper cites.
D. Deb, J. Zhang, and A. K. Jain, “Advfaces: Adversarial face synthesis,” in 2020 IEEE International Joint Conference on Biometrics (IJCB)
2020
Earlier work this paper cites.
H. Li, B. Li, S. Tan, and J. Huang, “Identification of deep network generated images using disparities in color components,” Signal Processing
2020
Earlier work this paper cites.
G. Pennycook and D. G. Rand, “The psychology of fake news,” Trends in cognitive sciences
2021
Cited alongside, same era.
N. Bonettini, P. Bestagini, S. Milani, and S. Tubaro, “On the use of benford’s law to detect gan-generated images,” in 2020 25th international conference on pattern recognition (ICPR)
2021
Cited alongside, same era.
M. Khosravy, K. Nakamura, Y. Hirose, N. Nitta, and N. Babaguchi, “Model inversion attack: analysis under gray-box scenario on deep learning based face recognition system,” KSII Transactions on Internet and Information Systems (TIIS)
2021
Cited alongside, same era.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning
2021
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Wang, Z. Wu, W. Ouyang, X. Han, J. Chen, Y.-G. Jiang, and S.-N. Li, “M2tr: Multi-modal multi-scale transformers for deepfake detection,” in Proceedings of the 2022 International Conference on Multimedia Retrieval
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A survey of convolutional neural networks: analysis, applications, and prospects,” IEEE transactions on neural networks and learning systems
2021
Cited alongside, same era.
K. Roose, “An ai-generated picture won an art prize. artists aren’t happy,” The New York Times
2022
Cited alongside, same era.
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
Cited alongside, same era.
B. Singh and D. K. Sharma, “Predicting image credibility in fake news over social media using multi-modal approach,” Neural Computing and Applications
2022
Cited alongside, same era.
2022
Cited alongside, same era.
P. Saikia, D. Dholaria, P. Yadav, V. Patel, and M. Roy, “A hybrid cnn-lstm model for video deepfake detection by leveraging optical flow features,” in 2022 International Joint Conference on Neural Networks (IJCNN)
2022
Later among the works it cites.
2022
Later among the works it cites.
J. J. Bird, A. Naser, and A. Lotfi, “Writer-independent signature verification; evaluation of robotic and generative adversarial attacks,” Information Sciences
2023
Closest in time.
2023
Closest in time.
F. Schneider, “Archisound: Audio generation with diffusion,” Master’s thesis, ETH Zurich, 2023
2023
Closest in time.
C. Guo, Y. Dou, T. Bai, X. Dai, C. Wang, and Y. Wen, “Artverse: A paradigm for parallel human–machine collaborative painting creation in metaverses,” IEEE Transactions on Systems, Man, and Cybernetics: Systems
2023
Closest in time.