Fetching the paper…
Reading the bibliography…
The generalization problem is broadly recognized as a critical challenge in detecting deepfakes.
The deepfake detection challenge (dfdc) preview dataset
Dolhansky, B.; Howes, R.; Pflaum, B.; Baram, N.; and Ferrer, C. C. 2019 · 1910
Earlier work this paper cites.
Faceshifter: Towards high fidelity and occlusion aware face swapping
Li, L.; Bao, J.; Yang, H.; Chen, D.; and Wen, F. 2019 · 1912
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J. 1992 · 1992
Earlier work this paper cites.
The deepfake detection challenge (dfdc) dataset
Dolhansky, B.; Bitton, J.; Pflaum, B.; Lu, J.; Howes, R.; Wang, M.; and Ferrer, C. C. 2020 · 2006
Earlier work this paper cites.
Visualizing data using t-SNE
Van der Maaten, L.; and Hinton, G. 2008 · 2008
Earlier work this paper cites.
Causality
Pearl, J. 2009 · 2009
Earlier work this paper cites.
Representation learning via invariant causal mechanisms
Mitrovic, J.; McWilliams, B.; Walker, J.; Buesing, L.; and Blundell, C. 2020 · 2010
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; and Batra, D. 2017 · 2017
Earlier work this paper cites.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Chattopadhay, A.; Sarkar, A.; Howlader, P.; and Balasubramanian, V. N. 2018 · 2018
Earlier work this paper cites.
Exposing deepfake videos by detecting face warping artifacts
Li, Y.; and Lyu, S. 2018 · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Oord, A. v. d.; Li, Y.; and Vinyals, O. 2018 · 2018
Earlier work this paper cites.
Faceforensics++: Learning to detect manipulated facial images
Rossler, A.; Cozzolino, D.; Verdoliva, L.; Riess, C.; Thies, J.; and Nießner, M. 2019 · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M.; and Le, Q. 2019 · 2019
Earlier work this paper cites.
Exposing Deep Fakes Using Inconsistent Head Poses
Yang, X.; Li, Y.; and Lyu, S. 2019 · 2019
Earlier work this paper cites.
What makes fake images detectable? understanding properties that generalize
Chai, L.; Bau, D.; Lim, S.-N.; and Isola, P. 2020 · 2020
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020 · 2020
Earlier work this paper cites.
On the detection of digital face manipulation
Dang, H.; Liu, F.; Stehouwer, J.; Liu, X.; and Jain, A. K. 2020 · 2020
Earlier work this paper cites.
Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection
Jiang, L.; Li, R.; Wu, W.; Qian, C.; and Loy, C. C. 2020 · 2020
Cited alongside, same era.
Self-supervised learning for generalizable out-of-distribution detection
Mohseni, S.; Pitale, M.; Yadawa, J.; and Wang, Z. 2020 · 2020
Cited alongside, same era.
Thinking in frequency: Face forgery detection by mining frequency-aware clues
Qian, Y.; Yin, G.; Sheng, L.; Chen, Z.; and Shao, J. 2020 · 2020
Cited alongside, same era.
AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
Sun, H.; Li, C.; Liu, B.; Liu, Z.; Wang, M.; Zheng, H.; Feng, D. D.; and Wang, S. 2020 · 2020
Cited alongside, same era.
Wilddeepfake: A challenging real-world dataset for deepfake detection
Zi, B.; Chang, M.; Chen, J.; Ma, X.; and Jiang, Y.-G. 2020 · 2020
Cited alongside, same era.
End-to-end reconstruction-classification learning for face forgery detection
Cao, J.; Ma, C.; Yao, T.; Chen, S.; Ding, S.; and Yang, X. 2022 · 2022
Later among the works it cites.
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Chen, L.; Zhang, Y.; Song, Y.; Liu, L.; and Wang, J. 2022 · 2022
Later among the works it cites.
Protecting celebrities from deepfake with identity consistency transformer
Dong, X.; Bao, J.; Chen, D.; Zhang, T.; Zhang, W.; Yu, N.; Chen, D.; Wen, F.; and Guo, B. 2022 · 2022
Later among the works it cites.
Wavelet-enhanced weakly supervised local feature learning for face forgery detection
Li, J.; Xie, H.; Yu, L.; and Zhang, Y. 2022 · 2022
Later among the works it cites.
Exploring Disentangled Content Information for Face Forgery Detection
Liang, J.; Shi, H.; and Deng, W. 2022 · 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…
Deepfakedetection. 2021 · 2021
Cited alongside, same era.
Spatiotemporal inconsistency learning for deepfake video detection
Gu, Z.; Chen, Y.; Yao, T.; Ding, S.; Li, J.; Huang, F.; and Ma, L. 2021 · 2021
Cited alongside, same era.
Lips Don’t Lie: A Generalisable and Robust Approach To Face Forgery Detection
Haliassos, A.; Vougioukas, K.; Petridis, S.; and Pantic, M. 2021 · 2021
Cited alongside, same era.
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W.; et al. 2021 · 2021
Cited alongside, same era.
Selecting data augmentation for simulating interventions
Ilse, M.; Tomczak, J. M.; and Forré, P. 2021 · 2021
Cited alongside, same era.
Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection
Li, J.; Xie, H.; Li, J.; Wang, Z.; and Zhang, Y. 2021 · 2021
Cited alongside, same era.
Generalizing Face Forgery Detection with High-frequency Features
Luo, Y.; Zhang, Y.; Yan, J.; and Liu, W. 2021 · 2021
Cited alongside, same era.
Shiohara, K.; and Yamasaki, T. 2022 · 2022
Later among the works it cites.
Face forgery detection via symmetric transformer
Song, L.; Li, X.; Fang, Z.; Jin, Z.; Chen, Y.; and Xu, C. 2022 · 2022
Later among the works it cites.
Dual contrastive learning for general face forgery detection
Sun, K.; Yao, T.; Chen, S.; Ding, S.; Li, J.; and Ji, R. 2022 · 2022
Later among the works it cites.
UIA-ViT: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection
Zhuang, W.; Chu, Q.; Tan, Z.; Liu, Q.; Yuan, H.; Miao, C.; Luo, Z.; and Yu, N. 2022 · 2022
Later among the works it cites.
Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
Dong, S.; Wang, J.; Ji, R.; Liang, J.; Fan, H.; and Ge, Z. 2023 · 2023
Later among the works it cites.
Implicit Identity Driven Deepfake Face Swapping Detection
Huang, B.; Wang, Z.; Yang, J.; Ai, J.; Zou, Q.; Wang, Q.; and Ye, D. 2023 · 2023
Later among the works it cites.
Region-aware pretraining for open-vocabulary object detection with vision transformers
Kim, D.; Angelova, A.; and Kuo, W. 2023 · 2023
Later among the works it cites.
SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
Larue, N.; Vu, N.-S.; Struc, V.; Peer, P.; and Christophides, V. 2023 · 2023
Later among the works it cites.
DenseDINO: boosting dense self-supervised learning with token-based point-level consistency
Yuan, Y.; Fu, X.; Yu, Y.; and Li, X. 2023 · 2023
Later among the works it cites.
Exposing the deception: Uncovering more forgery clues for deepfake detection
Ba, Z.; Liu, Q.; Liu, Z.; Wu, S.; Lin, F.; Lu, L.; and Ren, K. 2024 · 2024
Later among the works it cites.
Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection
Xu, Y.; Liang, J.; Sheng, L.; and Zhang, X.-Y. 2024 · 2024
Later among the works it cites.