A Deep Learning Approach to Image Splicing Using Depth Map
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-1018-0_34
Reference20 articles.
1. Salloum R, Ren Y, Jay Kuo C-C (2018) Image splicing localization using a multi-task fully convolutional network (MFCN). J Visual Commun Image Represent 51:201–209
2. Huh, M et al (2018) Fighting fake news: image splice detection via learned self-consistency. In: Proceedings of the European Conference on Computer Vision (ECCV)
3. Rao Y, Ni, J (2016) A deep learning approach to detection of splicing and copy-move forgeries in images. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE
4. Carvalho T et al (2018) Image Splicing detection through illumination inconsistencies and deep learning
5. CASIA v2.0 https://github.com/namtpham/casia2groundtruth
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1. High-Performance Image Splicing Detection utilizing Image Augmentation and Deep Learning;2023 IEEE 20th India Council International Conference (INDICON);2023-12-14
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