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Current Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in understanding multimodal data, but their potential remains underexplored for deepfake detection due to the misalignment of their knowledge and forensics patterns.
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
Earlier work this paper cites.
The dee pfake detection challenge (dfdc) pre view dataset
Dolhansky, B · 2019
Earlier work this paper cites.
Deepfakes detection dataset., 2019
Dufour, N. and Gully, A · 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.
The deepfake detection challenge (dfdc) dataset
Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., and Ferrer, C. C · 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
Earlier work this paper cites.
The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
Earlier work this paper cites.
Spatial-phase shallow learning: rethinking face forgery detection in frequency domain
Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., and Yu, N · 2021
Earlier work this paper cites.
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I · 2021
Earlier work this paper cites.
End-to-end reconstruction-classification learning for face forgery detection
Cao, J., Ma, C., Yao, T., Chen, S., Ding, S., and Yang, X · 2022
Earlier work this paper cites.
Frepgan: Robust deepfake detection using frequency-level perturbations
Jeong, Y., Kim, D., Ro, Y., and Choi, J · 2022
Earlier work this paper cites.
P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
Liu, X., Ji, K., Fu, Y., Tam, W., Du, Z., Yang, Z., and Tang, J · 2022
Earlier work this paper cites.
Detecting deepfakes with self-blended images
Shiohara, K. and Yamasaki, T · 2022
Earlier work this paper 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
Earlier work this paper cites.
Imagebind: One embedding space to bind them all
Girdhar, R., El-Nouby, A., Liu, Z., Singh, M., Alwala, K. V., Joulin, A., and Misra, I · 2023
Cited alongside, same era.
Implicit identity driven deepfake face swapping detection
Huang, B., Wang, Z., Yang, J., Ai, J., Zou, Q., Wang, Q., and Ye, D · 2023
Cited alongside, same era.
Winclip: Zero-/few-shot anomaly classification and segmentation
Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., and Dabeer, O · 2023
Cited alongside, same era.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Li, J., Li, D., Savarese, S., and Hoi, S · 2023
Cited alongside, same era.
Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection
Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., and Kot, A. C · 2023
Cited alongside, same era.
Pandagpt: One model to instruction-follow them all
Detecting and preventing hallucinations in large vision language models
Gunjal, A., Yin, J., and Bas, E · 2024
Later among the works it cites.
Ffaa: Multimodal large language model based explainable open-world face forgery analysis assistant
Huang, Z., Xia, B., Lin, Z., Mou, Z., and Yang, W · 2024
Later among the works it cites.
Clipping the deception: Adapting vision-language models for universal deepfake detection
Khan, S. A. and Dang-Nguyen, D.-T · 2024
Later among the works it cites.
Mitigating object hallucinations in large vision-language models through visual contrastive decoding
Leng, S., Zhang, H., Chen, G., Li, X., Lu, S., Miao, C., and Bing, L · 2024
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Laa-net: Localized artifact attention network for quality-agnostic and generalizable deepfake detection
Nguyen, D., Mejri, N., Singh, I. P., Kuleshova, P., Astrid, M., Kacem, A., Ghorbel, E., and Aouada, D · 2024
Later among the works it cites.
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Su, Y., Lan, T., Li, H., Xu, J., Wang, Y., and Cai, D · 2023
Cited alongside, same era.
Tall: Thumbnail layout for deepfake video detection
Xu, Y., Liang, J., Jia, G., Yang, Z., Zhang, Y., and He, R · 2023
Cited alongside, same era.
Ucf: Uncovering common features for generalizable deepfake detection
Yan, Z., Zhang, Y., Fan, Y., and Wu, B · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Large language models are visual reasoning coordinators
Chen, L., Li, B., Shen, S., Yang, J., Li, C., Keutzer, K., Darrell, T., and Liu, Z · 2024
Cited alongside, same era.
Can we leave deepfake data behind in training deepfake detector?
Cheng, J., Yan, Z., Zhang, Y., Luo, Y., Wang, Z., and Li, C · 2024
Cited alongside, same era.
Scaling rectified flow transformers for high-resolution image synthesis
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., et al · 2024
Cited alongside, same era.
Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection
Tan, C., Zhao, Y., Wei, S., Gu, G., Liu, P., and Wei, Y · 2024
Later among the works it cites.
Xu, Z., Zhang, X., Li, R., Tang, Z., Huang, Q., and Zhang, J · 2024
Later among the works it cites.
Transcending forgery specificity with latent space augmentation for generalizable deepfake detection
Yan, Z., Luo, Y., Lyu, S., Liu, Q., and Wu, B · 2024
Later among the works it cites.
Gpt4tools: Teaching large language model to use tools via self-instruction
Yang, R., Song, L., Li, Y., Zhao, S., Ge, Y., Li, X., and Shan, Y · 2024
Later among the works it cites.
Genface: A large-scale fine-grained face forgery benchmark and cross appearance-edge learning
Zhang, Y., Yu, Z., Wang, T., Huang, X., Shen, L., Gao, Z., and Ren, J · 2024
Later among the works it cites.
MiniGPT-4: Enhancing vision-language understanding with advanced large language models
Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M · 2024
Later among the works it cites.
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.
Standing on the shoulders of giants: Reprogramming visual-language model for general deepfake detection
Lin, K., Lin, Y., Li, W., Yao, T., and Li, B · 2025
Closest in time.