Fetching the paper…
Reading the bibliography…
With the advancement of deep learning-driven video editing technology, security risks have emerged.
H. Li and J. Huang, “Localization of deep inpainting using high-pass fully convolutional network,” in ICCV , Seoul, Korea (South), 2019, pp. 8300–8309
2019
Earlier work this paper cites.
S. W. Oh, S. Lee, J. Lee, and S. J. Kim, “Onion-peel networks for deep video completion,” in ICCV , Seoul, Korea (South), 2019, pp. 4402–4411
2019
Earlier work this paper cites.
S. Lee, S. W. Oh, D. Won, and S. J. Kim, “Copy-and-paste networks for deep video inpainting,” in ICCV , Seoul, Korea (South), 2019, pp. 4412–4420
2019
Earlier work this paper cites.
D. Kim, S. Woo, J. Lee, and I. S. Kweon, “Deep video inpainting,” in CVPR . Long Beach, CA, USA: IEEE, 2019, pp. 5792–5801
2019
Earlier work this paper cites.
R. Xu, X. Li, B. Zhou, and C. C. Loy, “Deep flow-guided video inpainting,” in CVPR . Long Beach, CA, USA: IEEE, 2019, pp. 3723–3732
2019
Earlier work this paper cites.
C. Gao, A. Saraf, J. Huang, and J. Kopf, “Flow-edge guided video completion,” in ECCV , vol. 12357, Glasgow, UK, 2020, pp. 713–729
2020
Earlier work this paper cites.
P. Zhou, B. Chen, X. Han, M. Najibi, A. Shrivastava, S. Lim, and L. Davis, “Generate, segment, and refine: Towards generic manipulation segmentation,” in AAAI , New York, NY, USA, 2020, pp. 13 058–13 065
2020
Earlier work this paper cites.
Y. Zeng, J. Fu, and H. Chao, “Learning joint spatial-temporal transformations for video inpainting,” in ECCV , vol. 12361, Glasgow, UK, 2020, pp. 528–543
2020
Earlier work this paper cites.
B. Yu, W. Li, X. Li, J. Lu, and J. Zhou, “Frequency-aware spatiotemporal transformers for video inpainting detection,” in ICCV , October 2021, pp. 8188–8197
2021
Earlier work this paper cites.
P. Zhou, N. Yu, Z. Wu, L. Davis, A. Shrivastava, and S. Lim, “Deep video inpainting detection,” in 32nd British Machine Vision Conference 2021, BMVC , Online, 2021, p. 35
2021
Earlier work this paper cites.
Y. Rao and J. Ni, “Self-supervised domain adaptation for forgery localization of JPEG compressed images,” in ICCV , Montreal, QC, Canada, 2021, pp. 15 014–15 023
2021
Earlier work this paper cites.
Y. Yuan, R. Fu, L. Huang, W. Lin, C. Zhang, X. Chen, and J. Wang, “Hrformer: High-resolution vision transformer for dense predict,” in NeurIPS , Virtual, 2021, pp. 7281–7293
2021
Cited alongside, same era.
Y. Niu, B. Tondi, Y. Zhao, R. Ni, and M. Barni, “Image splicing detection, localization and attribution via JPEG primary quantization matrix estimation and clustering,” IEEE Trans. Inf. Forensics Secur. , vol. 16, pp. 5397–5412, 2021
2021
Cited alongside, same era.
X. Jin, Z. He, J. Xu, Y. Wang, and Y. Su, “Video splicing detection and localization based on multi-level deep feature fusion and reinforcement learning,” Multim. Tools Appl. , vol. 81, no. 28, pp. 40 993–41 011, 2022
2022
Cited alongside, same era.
X. Xiang, Y. Zhang, L. Jin, Z. Li, and J. Tang, “Sub-region localized hashing for fine-grained image retrieval,” IEEE TIP , vol. 31, pp. 314–326, 2022
2022
Cited alongside, same era.
P. Pei, X. Zhao, J. Li, Y. Cao, and X. Lai, “Vision transformer based video hashing retrieval for tracing the source of fake videos,” Security and Communication Networks , vol. 2023, 2023
2023
Closest in time.
H. Ding, L. Chen, Q. Tao, Z. Fu, L. Dong, and X. Cui, “Dcu-net: a dual-channel u-shaped network for image splicing forgery detection,” Neural Comput. Appl. , vol. 35, no. 7, pp. 5015–5031, 2023
2023
Closest in time.
C. Yan, S. Li, and H. Li, “Transu2-net: A hybrid transformer architecture for image splicing forgery detection,” IEEE Access , vol. 11, pp. 33 313–33 323, 2023
2023
Closest in time.
P. Pei, L. Guoqing, and L. Tao, “Multi-view inconsistency analysis for video object-level splicing localization,” International Journal of Emerging Technologies and Advanced Applications , vol. 1, no. 3, p. 1–5, Apr. 2024
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Wei, H. Li, and J. Huang, “Deep video inpainting localization using spatial and temporal traces,” in ICASSP , 2022, pp. 8957–8961
2022
Cited alongside, same era.
Q. Diao, Y. Jiang, B. Wen, J. Sun, and Z. Yuan, “Metaformer: A unified meta framework for fine-grained recognition,” in CVPR . New Orleans, Louisiana, USA: IEEE, 2022
2022
Cited alongside, same era.
P. Pei, X. Zhao, J. Li, and Y. Cao, “VIFST: video inpainting localization using multi-view spatial-frequency traces,” in PRICAI 2023 , vol. 14327. Jakarta, Indonesia: Springer, 2023, pp. 434–446
2023
Cited alongside, same era.
C. Dong, X. Chen, R. Hu, J. Cao, and X. Li, “Mvss-net: Multi-view multi-scale supervised networks for image manipulation detection,” IEEE TPAMI , vol. 45, no. 3, pp. 3539–3553, 2023
2023
Cited alongside, same era.
P. Pei, X. Zhao, J. Li, and Y. Cao, “Uvl: A unified framework for video tampering localization,” 2023
2023
Cited alongside, same era.
Y. Zhang, Z. Fu, S. Qi, M. Xue, Z. Hua, and Y. Xiang, “Localization of inpainting forgery with feature enhancement network,” IEEE Transactions on Big Data , vol. 9, no. 3, pp. 936–948, 2023
2023
Cited alongside, same era.
J. Li, X. Zhao, and Y. Cao, “Generalizable deep video inpainting detection based on constrained convolutional neural networks,” in Digital Forensics and Watermarking - 22nd International Workshop, IWDW 2023, Jinan, China, November 25-26, 2023, Revised Selected Papers , ser. Lecture Notes in Computer Science, vol. 14511. Springer, 2023, pp. 125–138. [Online]. Available: https://doi.org/10.1007/978-981-97-2585-4\_9
2023
Cited alongside, same era.
2024
Closest in time.
2024
Closest in time.
C. Yan, H. Wei, Z. Lan, and H. Li, “Msa-net: Multi-scale attention network for image splicing localization,” Multim. Tools Appl. , vol. 83, no. 7, pp. 20 587–20 604, 2024. [Online]. Available: https://doi.org/10.1007/s11042-023-16131-0
2024
Closest in time.
2024
Closest in time.
A. Zayed, G. Mordido, S. Shabanian, I. Baldini, and S. Chandar, “Fairness-aware structured pruning in transformers,” in Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, February 20-27, 2024, Vancouver, Canada , M. J. Wooldridge, J. G. Dy, and S. Natarajan, Eds. AAAI Press, 2024, pp. 22 484–22 492. [Online]. Available: https://doi.org/10.1609/aaai.v38i20.30256
2024
Closest in time.
D. Dordevic, V. Bozic, J. Thommes, D. Coppola, and S. P. Singh, “Rethinking attention: Exploring shallow feed-forward neural networks as an alternative to attention layers in transformers (student abstract),” in Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, February 20-27, 2024, Vancouver, Canada , M. J. Wooldridge, J. G. Dy, and S. Natarajan, Eds. AAAI Press, 2024, pp. 23 477–23 479. [Online]. Available: https://doi.org/10.1609/aaai.v38i21.30436
2024
Closest in time.