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In recent years, many forensic detectors have been proposed to detect AI-generated images and prevent their use for malicious purposes.
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2021
Cited alongside, same era.
Y. Wang, X. Ding, L. Ding, R. Ward, and Z. J. Wang, “Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection,” Pattern Recognition Letters , vol. 146, pp. 15–22, 2021
2021
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Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A ConvNet for the 2020s,” in CVPR , 2022, pp. 11 976–11 986
2022
Cited alongside, same era.
M. Barni, W. Li, B. Tondi, and B. Zhang, “Adversarial Examples in Image Forensics,” in Multimedia Forensics . Springer, 2022
2022
Cited alongside, same era.
S. Jia, C. Ma, T. Yao, B. Yin, S. Ding, and X. Yang, “Exploring Frequency Adversarial Attacks for Face Forgery Detection,” in CVPR , 2022, pp. 4103–4112
2022
Cited alongside, same era.
X. Zhao and M. C. Stamm, “Making Generated Images Hard To Spot: A Transferable Attack On Synthetic Image Detectors,” in ICPR , 2022
2022
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R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, and L. Verdoliva, “On the detection of synthetic images generated by diffusion models,” in ICASSP , 2023, pp. 1–5
2023
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2023
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2024
Closest in time.
D. Tariang, R. Corvi, D. Cozzolino, G. Poggi, K. Nagano, and L. Verdoliva, “Synthetic Image Verification in the Era of Generative AI: What Works and What Isn’t There Yet,” IEEE Security & Privacy , vol. 22, pp. 37–49, 2024
2024
Closest in time.
D. Cozzolino, G. Poggi, R. Corvi, M. Nießner, and L. Verdoliva, “Raising the Bar of AI-generated Image Detection with CLIP,” in CVPR Workshop , 2024, pp. 4356–4366
2024
Closest in time.