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An image copyright protection and tampering detection scheme based on deep learningand memristor

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Category: Information technologies, systems analysis and administration

Last Updated on 19 November 2016

Published on 17 November 2016

Hits: 4160

Authors:

Changjiu Pu, Chongqing University of Education, Chongqing, China

Fei Hu, Chongqing University of Education, Chongqing, China

Jie Long, Chongqing University of Education, Chongqing, China

Abstract:

Purpose. In order to improve the effect of image copyright protection and detect whether an image is tampered illegally, we introduce an image copyright protection and tampering detection scheme of ROI (Region of interest) image based on image feature sequence in NROI (Non Region of interest) image. We have evaluated this scheme with some performance measures and the results show it is effective.

Methodology. We formulate the scheme using the copyright watermarking and the fragile watermarking. With the deep learning, memristor chaos, Arnold transform and extend zigzag transform, the watermarkings are generated and embedded into ROI image in DCT (Discrete cosine transform) domain using the feature sequence of NROI image.

Findings. We first completed the division of ROI and NROI image and get the feature sequence of NROI image using deep learning and memristor chaos. Then by using the sequence and some methods such as Arnold transform, we obtained the scrambling copyright watermarking and the new fragile watermarking of each image grouping and embedded them into ROI image.

Originality. We realize the extraction of image feature sequence in NROI image using deep learning and memristor chaos. It is applied to generate and embed the scrambling copyright watermarking and the new fragile watermarking into ROI image. The research on this aspect has not been found at present.

Practical value. We have completed some validation experiments with some performance measures. The results show it can completely satisfy the need of secure transmission. This scheme features strong robustness and security.

References/Список літератури

1. Umaamaheshvari, A. and Thanushkodi, K., 2014. Image Security Through Watermarking. Journal of Medical Imaging and Health Informatic, Vol. 4, No. 2, pp. 277–284.

2. Mettripun Narong, Amornraksa Thumrongrat and Delp Edward J., 2013. Robust image watermarking based on luminance modification. Journal of Electronic Imaging, Vol. 22, No. 3, pp. 1090–1098.

3. Masoodhu Banu, N. M. and Sujatha, S., 2015. Improved Tampering Detection for Image Authentication Based on Image Partitioning. Wireless Personal Communications, Vol. 84, No. 1, pp. 69–85.

4. Lyu Wan-Li, Chang Chin-Chen, and Chou Yeh-Chieh, 2015. Hybrid color image steganography method used for copyright protection and content authentication. Journal of Information Hiding and Multimedia Signal Processing, Vol. 6, No. 4, pp. 686–696.

5. Yu Hongyang, Yang Gongping, Wang Zhuoyi and Lin Zhang, 2015. A New Finger-Knuckle-Print ROI Extraction Method Based on Two-Stage Center Point Detection. International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 8, No. 2, pp. 185–200.

6. Göpfert, F., Schmidt, R., Wulff, J. and Zink, K., 2015. Effect of ROI filtering in 3D cone-beam rotational angiography on organ dose and effective dose in cerebral investigations. Journal of Applied Clinical Medical Physics, Vol. 16, No. 2, pp. 229–249.

7. S Bu, Z Liu, J Han, et al., 2014. Learning High-Level Feature by Deep Belief Networks for 3-D Model Retrieval and Recognition», IEEE Transactions on Multimedia, Vol. 16, No. 8, pp. 2154–2167.

8. Yushi Chen, Xing Zhao and Xiuping Jia, 2015. Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 6, pp. 1939–1404.

9. Rakkiyappan, R., Sivasamy, R. and Li Xiaodi, 2015. Synchronization of Identical and Nonidentical Memristor-based Chaotic Systems Via Active Backstepping Control Technique. Circuits, Systems, and Signal Processing, Vol. 34, No. 3, pp. 763–778.

10. Mo Chen, Mengyuan Li, Qing Yu, Bocheng Bao, Quan Xu and Jiang Wang, 2015. Dynamics of self-excited attractors and hidden attractors in generalized memristor-based Chua’s circuit. Nonlinear Dynamics, Vol. 81, No. 1, pp. 215–226.

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