Fetching the paper…
Reading the bibliography…
AI-generated images (AIGIs), such as natural or face images, have become increasingly important yet challenging.
Rademacher complexity bounds for non-iid processes
Mohri, M. and Rostamizadeh, A · 2008
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Face2face: Real-time face capture and reenactment of rgb videos
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., and Nießner, M · 2016
Earlier work this paper cites.
Photographic image synthesis with cascaded refinement networks
Chen, Q. and Koltun, V · 2017
Earlier work this paper cites.
Implicit regularization in matrix factorization
Gunasekar, S., Woodworth, B. E., Bhojanapalli, S., Neyshabur, B., and Srebro, N · 2017
Earlier work this paper cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A · 2017
Earlier work this paper cites.
Learning to see in the dark
Chen, C., Chen, Q., Xu, J., and Koltun, V · 2018
Earlier work this paper cites.
Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., and Choo, J · 2018
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
Earlier work this paper cites.
Deepfakes: a new threat to face recognition? assessment and detection
Korshunov, P. and Marcel, S · 2018
Earlier work this paper cites.
Learning to generalize: Meta-learning for domain generalization
Li, D., Yang, Y., Song, Y.-Z., and Hospedales, T · 2018
Earlier work this paper cites.
Second-order attention network for single image super-resolution
Dai, T., Cai, J., Zhang, Y., Xia, S.-T., and Lei, Z · 2019
Earlier work this paper cites.
The deepfake detection challenge (dfdc) preview dataset
Dolhansky, B., Howes, R., Pflaum, B., Baram, N., and Ferrer, C. C · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Earlier work this paper cites.
Diverse image synthesis from semantic layouts via conditional imle
Li, K., Zhang, T., and Malik, J · 2019
Earlier work this paper cites.
Detecting gan generated fake images using co-occurrence matrices
Nataraj, L., Mohammed, T. M., Chandrasekaran, S., Flenner, A., Bappy, J. H., Roy-Chowdhury, A. K., and Manjunath, B · 2019
Earlier work this paper cites.
Semantic image synthesis with spatially-adaptive normalization
Park, T., Liu, M.-Y., Wang, T.-C., and Zhu, J.-Y · 2019
Earlier work this paper cites.
FaceForensics++: Learning to detect manipulated facial images
Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Nießner, M · 2019
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
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
Earlier work this paper cites.
Detecting and simulating artifacts in gan fake images
Zhang, X., Karaman, S., and Chang, S.-F · 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
Earlier work this paper cites.
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 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
Earlier work this paper cites.
Oc-fakedect: Classifying deepfakes using one-class variational autoencoder
Khalid, H. and Woo, S. S · 2020
Earlier work this paper cites.
Global texture enhancement for fake face detection in the wild
Liu, Z. et al · 2020
Earlier work this paper cites.
Thinking in frequency: Face forgery detection by mining frequency-aware clues
Qian, Y., Yin, G., Sheng, L., Chen, Z., and Shao, J · 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
Cited alongside, same era.
https://www.kaggle.com/c/deepfake-detection-challenge Accessed 2021-04-24
detection challenge., D., 2020 · 2021
Cited alongside, same era.
https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html Accessed 2021-04-24
DFD., 2020 · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. et al · 2021
Cited alongside, same era.
Detecting deepfakes with self-blended images
Shiohara, K. and Yamasaki, T · 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
Later among the works it cites.
M2tr: Multi-modal multi-scale transformers for deepfake detection
Wang, J., Wu, Z., Ouyang, W., Han, X., Chen, J., Jiang, Y.-G., and Li, S.-N · 2022
Later among the works it cites.
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al · 2023
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
Later among the works it cites.
Implicit identity driven deepfake face swapping detection
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lips don’t lie: A generalisable and robust approach to face forgery detection
Haliassos, A., Vougioukas, K., Petridis, S., and Pantic, M · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 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
Cited alongside, same era.
Generalizing face forgery detection with high-frequency features
Luo, Y., Zhang, Y., Yan, J., and Liu, W · 2021
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
Huang, B., Wang, Z., Yang, J., Ai, J., Zou, Q., Wang, Q., and Ye, D · 2023
Later among the works it cites.
Enhancing general face forgery detection via vision transformer with low-rank adaptation
Kong, C., Li, H., and Wang, S · 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
Later among the works it cites.
F 2 trans: High-frequency fine-grained transformer for face forgery detection
Miao, C., Tan, Z., Chu, Q., Liu, H., Hu, H., and Yu, N · 2023
Later among the works it cites.
Towards universal fake image detectors that generalize across generative models
Ojha, U. et al · 2023
Later among the works it cites.
Deepfake-adapter: Dual-level adapter for deepfake detection
Shao, R., Wu, T., Nie, L., and Liu, Z · 2023
Later among the works it cites.
Blendface: Re-designing identity encoders for face-swapping
Shiohara, K., Yang, X., and Taketomi, T · 2023
Later among the works it cites.
Learning on gradients: Generalized artifacts representation for gan-generated images detection
Tan, C., Zhao, Y., Wei, S., Gu, G., and Wei, Y · 2023
Later among the works it cites.
Generalizable synthetic image detection via language-guided contrastive learning
Wu, H., Zhou, J., and Zhang, S · 2023
Later among the works it cites.
Tall: Thumbnail layout for deepfake video detection
Xu, Y., Liang, J., Jia, G., Yang, Z., Zhang, Y., and He, R · 2023
Later among the works it cites.
Sigmoid loss for language image pre-training
Zhai, X., Mustafa, B., Kolesnikov, A., and Beyer, L · 2023
Later among the works it cites.
Drct: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images
Chen, B., Zeng, J., Yang, J., and Yang, R · 2024
Closest in time.
Can we leave deepfake data behind in training deepfake detector?
Cheng, J., Yan, Z., Zhang, Y., Luo, Y., Wang, Z., and Li, C · 2024
Closest in time.
Exploiting style latent flows for generalizing deepfake video detection
Choi, J., Kim, T., Jeong, Y., Baek, S., and Choi, J · 2024
Closest in time.
Moe-ffd: Mixture of experts for generalized and parameter-efficient face forgery detection
Kong, C., Luo, A., Bao, P., Yu, Y., Li, H., Zheng, Z., Wang, S., and Kot, A. C · 2024
Closest in time.
Lin, Y., Song, W., Li, B., Li, Y., Ni, J., Chen, H., and Li, Q · 2024
Closest in time.
Forgery-aware adaptive transformer for generalizable synthetic image detection
Liu, H., Tan, Z., Tan, C., Wei, Y., Wang, J., and Zhao, Y · 2024
Closest in time.
Lare2̂: Latent reconstruction error based method for diffusion-generated image detection
Luo, Y., Du, J., Yan, K., and Ding, S · 2024
Closest in time.
Learning natural consistency representation for face forgery video detection
Zhang, D., Xiao, Z., Li, S., Lin, F., Li, J., and Ge, S · 2024
Closest in time.
Genimage: A million-scale benchmark for detecting ai-generated image
Zhu, M., Chen, H., Yan, Q., Huang, X., Lin, G., Li, W., Tu, Z., Hu, H., Hu, J., and Wang, Y · 2024
Closest in time.
Exploring unbiased deepfake detection via token-level shuffling and mixing
Fu, X., Yan, Z., Yao, T., Chen, S., and Li, X · 2025
Closest in time.
Gpt-imgeval: A comprehensive benchmark for diagnosing gpt4o in image generation
Yan, Z., Ye, J., Li, W., Huang, Z., Yuan, S., He, X., Lin, K., He, J., He, C., and Yuan, L · 2025
Closest in time.