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Digital video inpainting techniques have been substantially improved with deep learning in recent years.
B. Mahdian and S. Saic, “Using noise inconsistencies for blind image forensics,” Image Vis. Comput. , vol. 27, no. 10, pp. 1497–1503, 2009
2009
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J. Fridrich and J. Kodovsky, “Rich models for steganalysis of digital images,” IEEE Trans. Inf. Forensics Security , vol. 7, no. 3, pp. 868–882, 2012
2012
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P. Ferrara, T. Bianchi, A. De Rosa, and A. Piva, “Image forgery localization via fine-grained analysis of cfa artifacts,” IEEE Trans. Inf. Forensics Security , vol. 7, no. 5, pp. 1566–1577, 2012
2012
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S. Chen, S. Tan, B. Li, and J. Huang, “Automatic detection of object-based forgery in advanced video,” IEEE Trans. Circuits Syst. Video Technol. , vol. 26, no. 11, pp. 2138–2151, 2015
2015
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C. Feng, Z. Xu, S. Jia, W. Zhang, and Y. Xu, “Motion-adaptive frame deletion detection for digital video forensics,” IEEE Trans. Circuits Syst. Video Technol. , vol. 27, no. 12, pp. 2543–2554, 2016
2016
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F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, “A benchmark dataset and evaluation methodology for video object segmentation,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 724–732
2016
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H. Li, W. Luo, and J. Huang, “Localization of diffusion-based inpainting in digital images,” IEEE Trans. Inf. Forensics Security , vol. 12, no. 12, pp. 3050–3064, 2017
2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proc. IEEE Int. Conf. Comput. Vis. , 2017, pp. 2980–2988
2017
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2017
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L. D’Amiano, D. Cozzolino, G. Poggi, and L. Verdoliva, “A patchmatch-based dense-field algorithm for video copy–move detection and localization,” IEEE Trans. Circuits Syst. Video Technol. , vol. 29, no. 3, pp. 669–682, 2018
2018
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P. Zhou, X. Han, V. I. Morariu, and L. S. Davis, “Learning rich features for image manipulation detection,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 1053–1061
2018
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B. Bayar and M. C. Stamm, “Constrained convolutional neural networks: A new approach towards general purpose image manipulation detection,” IEEE Trans. Inf. Forensics Security , vol. 13, no. 11, pp. 2691–2706, 2018
2018
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N. Xu, L. Yang, Y. Fan, J. Yang, D. Yue, Y. Liang, B. Price, S. Cohen, and T. Huang, “Youtube-vos: Sequence-to-sequence video object segmentation,” in Proc. Eur. Conf. Comput. Vis. , September 2018
2018
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Y.-L. Chang, Z. Y. Liu, K.-Y. Lee, and W. Hsu, “Free-form video inpainting with 3d gated convolution and temporal patchgan,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 9066–9075
2019
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C. Wang, H. Huang, X. Han, and J. Wang, “Video inpainting by jointly learning temporal structure and spatial details,” in Proc. AAAI Conf. Artif. Intell. , vol. 33, no. 01, 2019, pp. 5232–5239
2019
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S. Lee, S. W. Oh, D. Won, and S. J. Kim, “Copy-and-paste networks for deep video inpainting,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 4413–4421
2019
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S. W. Oh, S. Lee, J.-Y. Lee, and S. J. Kim, “Onion-peel networks for deep video completion,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 4403–4412
2019
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H. Li and J. Huang, “Localization of deep inpainting using high-pass fully convolutional network,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 8301–8310
2019
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Y. Wu, W. AbdAlmageed, and P. Natarajan, “Mantra-net: Manipulation tracing network for detection and localization of image forgeries with anomalous features,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 9543–9552
2019
Cited alongside, same era.
J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu, “Dual attention network for scene segmentation,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 3146–3154
2019
Cited alongside, same era.
X. Lu, W. Wang, C. Ma, J. Shen, L. Shao, and F. Porikli, “See more, know more: Unsupervised video object segmentation with co-attention siamese networks,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 3623–3632
2019
Cited alongside, same era.
M. Liao, F. Lu, D. Zhou, S. Zhang, W. Li, and R. Yang, “Dvi: Depth guided video inpainting for autonomous driving,” in Proc. Eur. Conf. Comput. Vis. , 2020, pp. 1–17
2020
Cited alongside, same era.
L. Huang, X. Zhao, and K. Huang, “Got-10k: A large high-diversity benchmark for generic object tracking in the wild,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 43, no. 5, pp. 1562–1577, 2021
2021
Later among the works it cites.
Z. Li, C.-Z. Lu, J. Qin, C.-L. Guo, and M.-M. Cheng, “Towards an end-to-end framework for flow-guided video inpainting,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 17 562–17 571
2022
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K. Zhang, J. Fu, and D. Liu, “Inertia-guided flow completion and style fusion for video inpainting,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 5982–5991
2022
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K. Zhang, J. Fu, and D. Liu, “Flow-guided transformer for video inpainting,” in Proc. Eur. Conf. Comput. Vis. , 2022, pp. 74–90
2022
Later among the works it cites.
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D. Kim, S. Woo, J.-Y. Lee, and I. S. Kweon, “Recurrent temporal aggregation framework for deep video inpainting,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 42, no. 5, pp. 1038–1052, 2020
2020
Cited alongside, same era.
C. Gao, A. Saraf, J.-B. Huang, and J. Kopf, “Flow-edge guided video completion,” in Proc. Eur. Conf. Comput. Vis. , 2020, pp. 713–729
2020
Cited alongside, same era.
Y. Zeng, J. Fu, and H. Chao, “Learning joint spatial-temporal transformations for video inpainting,” in Proc. Eur. Conf. Comput. Vis. , 2020, pp. 528–543
2020
Cited alongside, same era.
P. Zhou, B.-C. Chen, X. Han, M. Najibi, A. Shrivastava, S.-N. Lim, and L. Davis, “Generate, segment, and refine: Towards generic manipulation segmentation,” in Proc. AAAI Conf. Artif. Intell. , vol. 34, no. 07, 2020, pp. 13 058–13 065
2020
Cited alongside, same era.
M. Aloraini, M. Sharifzadeh, and D. Schonfeld, “Sequential and patch analyses for object removal video forgery detection and localization,” IEEE Trans. Circuits Syst. Video Technol. , vol. 31, no. 3, pp. 917–930, 2020
2020
Cited alongside, same era.
C. Yang, H. Li, F. Lin, B. Jiang, and H. Zhao, “Constrained r-cnn: A general image manipulation detection model,” in IEEE Int. Conf. Multimed. Expo , 2020, pp. 1–6
2020
Cited alongside, same era.
J. Fu, J. Liu, J. Jiang, Y. Li, Y. Bao, and H. Lu, “Scene segmentation with dual relation-aware attention network,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 32, no. 6, pp. 2547–2560, 2020
2020
Cited alongside, same era.
R. Liu, H. Deng, Y. Huang, X. Shi, L. Lu, W. Sun, X. Wang, J. Dai, and H. Li, “Fuseformer: Fusing fine-grained information in transformers for video inpainting,” in Proc. IEEE Int. Conf. Comput. Vis. , 2021, pp. 14 040–14 049
2021
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 Trans. Pattern Anal. Mach. Intell. , vol. 45, no. 3, pp. 3539–3553, 2022
2022
Later among the works it cites.
H. Wu, J. Zhou, J. Tian, J. Liu, and Y. Qiao, “Robust image forgery detection against transmission over online social networks,” IEEE Trans. Inf. Forensics Security , vol. 17, pp. 443–456, 2022
2022
Later among the works it cites.
S. Wei, H. Li, and J. Huang, “Deep video inpainting localization using spatial and temporal traces,” in IEEE Int. Conf. Acoust. Speech Signal Process. , 2022, pp. 8957–8961
2022
Later among the works it cites.
2022
Later among the works it cites.
H. K. Cheng and A. G. Schwing, “Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model,” in Proc. Eur. Conf. Comput. Vis. , 2022
2022
Later among the works it cites.
F. Guillaro, D. Cozzolino, A. Sud, N. Dufour, and L. Verdoliva, “Trufor: Leveraging all-round clues for trustworthy image forgery detection and localization,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2023, pp. 20 606–20 615
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Lin, S. Wang, J. Deng, Y. Fu, X. Bai, X. Chen, X. Qu, and W. Tang, “Image manipulation detection by multiple tampering traces and edge artifact enhancement,” Pattern Recognition , vol. 133, p. 109026, 2023
2023
Later among the works it cites.
P. Pei, X. Zhao, J. Li, and Y. Cao, “Vifst: Video inpainting localization using multi-view spatial-frequency traces,” in Pacific Rim International Conference on Artificial Intelligence , 2023, pp. 434–446
2023
Later among the works it cites.
——, “Uvl: A unified framework for video tampering localization,” arXiv:2309.16126 , 2023
2023
Later among the works it cites.
K. Li, Y. Wang, J. Zhang, P. Gao, G. Song, Y. Liu, H. Li, and Y. Qiao, “Uniformer: Unifying convolution and self-attention for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , 2023
2023
Later among the works it cites.
H. Ding, C. Liu, S. He, X. Jiang, P. H. Torr, and S. Bai, “MOSE: A new dataset for video object segmentation in complex scenes,” in Proc. IEEE Int. Conf. Comput. Vis. , 2023
2023
Later among the works it cites.
Y. Yao, T. Han, X. Gao, Y. Ren, and W. Meng, “Deep video inpainting detection and localization based on convnext dual-stream network,” Expert Systems with Applications , vol. 247, p. 123331, 2024
2024
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