ARCHIVES

1. Z. Meng, T. Morizumi, S. Miyata and H. Kinoshita, “An Improved Design Scheme for Perceptual Hashing Based on CNN for Digital Watermarking,” in Proceedings of the IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), 2020, pp. 1789–1794.
2. D. Cozzolino, G. Poggi and L. Verdoliva, “Recasting Residual-Based Local Descriptors as Convolutional Neural Networks: An Application to Image Forgery Detection,” IEEE Signal Processing Letters, 2017.
3. M. Liu, H. Gao and X. Xia, “Perceptual Image Hashing Based on Canny Operator and Tensor for Copy-Move Forgery Detection,” The Computer Journal, vol. 67, no. 2, pp. 447–460, 2022.
4. Y. Luo, Y. Zhang, J. Yan and W. Liu, “Generalizing Face Forgery Detection with High-Frequency Features,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021.
5. S. Bayar and M. C. Stamm, “A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer,” ACM Workshop on Information Hiding and Multimedia Security, 2016.
6. J. Zeng, “A Novel Block-DCT and PCA Based Image Perceptual Hashing Algorithm,” International Journal of Computer Science, 2013.
7. S. Singh and R. Kumar, “Digital Image Forgery Detection using Convolutional Neural Networks,” in IEEE International Conference on Artificial Intelligence and Smart Systems, 2023.
8. M. Hussain et al., “Image Forgery Detection using Error Level Analysis and CNN,” in IEEE International Conference on Computing and Communication Technologies, 2023.
9. M. Zauner, “Implementation and Benchmarking of Perceptual Image Hash Functions,” Master’s Thesis, Upper Austria University of Applied Sciences, 2010.
10. X. Liu et al., “Effective Near-Duplicate Image Detection using Perceptual Hashing and Deep Learning,” Information Processing & Management, 2025.