Computer Graphic and Photographic Images Classification—A Transfer Learning Approach
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-0189-0_44
Reference18 articles.
1. Meena KB, Tyagi V (2019) Methods to distinguish photorealistic computer generated images from photographic images: a review. In: Advances in computing and data sciences, vol 1. Springer Nature Singapore Pte Ltd., pp 64–82. https://doi.org/10.1007/978-981-13-9939-8_7
2. Meena KB, Tyagi V (2019) A copy-move image forgery detection technique based on gaussian-hermite moments. Multimed Tools Appl 78:33505–33526. https://doi.org/10.1007/s11042-019-08082-2
3. Ni X, Chen L, Yuan L, Wu G, Yao Y (2019) An Evaluation of deep learning-based computer generated image detection approaches. IEEE Access 7:130830–130840. https://doi.org/10.1109/ACCESS.2019.2940383
4. Quan W, Wang K, Yan D-M, Zhang X, Pellerin D (2020) Learn with diversity and from harder samples: Improving the generalization of CNN-based detection of computer-generated images. Forensic Sci Int Digit Investig 35:301023. https://doi.org/10.1016/j.fsidi.2020.301023
5. Lyu S, Farid H (2005) How realistic is photorealistic? IEEE Trans Signal Process 53:845–850. https://doi.org/10.1109/TSP.2004.839896
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