Abstract
With the development of multimedia technology, the difficulty of image tampering has been reduced in recent years. Propagation of tampered images brings many adverse effects so that the technology of image tamper detection needs to be urgently developed. A faster-rcnn based image tamper localization recognition method with dual-flow Discrete Cosine Transform (DCT) high-frequency and low-frequency input is presented. For capturing subtle transform edges not visible in RGB domain, we extract high-frequency features from the image as an additional data stream embedding model. Our network model uses low-frequency images as the subject data to detect object consistency in different regions, further complements high-rate streams to strengthen image region consistency detection, and complements duplicate stream object tampering detection. Extensive experiments are performed on the CASIA V2.0 image dataset. These results demonstrate that faster-rcnn-w outperforms existing mainstream image tampering detection methods in different evaluation indicators.
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Data Availability
The data in the experiments, used to support the findings of this study are available from the corresponding author upon request.
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Acknowledgment
This work has been supported in part by the Natural Science Foundation of China under grant No. 62202211, the project supported by National Social Science Foundation under Grant No. 19CTJ014, the Science and Technology Research Project of Jiangxi Provincial Department of Education (No. GJJ170234).
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Deng, L., Peng, J., Deng, W., Liu, K., Cao, Z., Wang, W. (2023). A Dual-Stream Input Faster-CNN Model for Image Forgery Detection. In: Cao, Y., Shao, X. (eds) Mobile Networks and Management. MONAMI 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-031-32443-7_7
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