Abstract
With the introduction of AI technology in the field of image inpainting, it makes up for the shortcomings of traditional inpainting methods. Since this technology can achieve object removal without obvious traces by learning visual semantics informations and performs better in complex environment, it brings great challenges to the existing image forensics work. So far, there are few detection methods for AI image inpainting forgery. In order to fill the gap in this field, this paper proposes a deep learning-based detection method. Considering Faster R-CNN model has demonstrated good performance on detecting semantic objects over a range of scales, we migrate it to the forensic field for image inpainting forgery. It turns out that Faster R-CNN model can capture inconsistencies features between the inpainted region and the authentic region. Therefore, this can further enrich the application range of digital image forensics algorithms. We construct a large-scale AI image inpainting dataset based on ImageNet dataset. The experimental results on this dataset demonstrate that our proposed approach achieves good performance.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (No. 61370195, U1536121).
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Wang, X., Wang, H., Niu, S. (2019). An Image Forensic Method for AI Inpainting Using Faster R-CNN. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11634. Springer, Cham. https://doi.org/10.1007/978-3-030-24271-8_43
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DOI: https://doi.org/10.1007/978-3-030-24271-8_43
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