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Chandrasekaran, Venkatachalam
- A Novel 3-Level DWT and CNN-Based Blind Grayscale Image Watermarking for Copyright Protection Against Adversarial Attacks
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1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, IN
1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, IN
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 4 (2021), Pagination: 2460-2469Abstract
Copyright protection of digital images is an important commercial requirement to individual artists and large organisations alike. Wavelet-based image watermarking methods have been in practice due to their robustness against standard geometrical and image processing attacks. Convolutional Neural Networks (CNNs)-based watermarking methods are becoming popular as they provide a new dimension to the generation of a watermarked image, which is perceptually close to the original image when trained over a large class of images, thereby eliminating the need to train on each image that is to be watermarked. However, the watermark extraction performance of CNNs when used in standalone mode reduces in the presence of adversarial examples. In this study, we combine the robustness of a multi-level Discrete Wavelet Transform (DWT) and the power of CNNs and propose a robust blind grayscale image watermarking method. In the proposed method watermark is of the same size as the original image thereby demonstrating the robustness under increased payload as well. The quality of the extracted watermark is measured using Structural Similarity Index Measure (SSIM), Peak-Signal-to-Noise ratio (PSNR) and Normalized Cross Correlation (NCC). Our proposed method provides high quality watermark extraction under geometrical, image processing and adversarial attacks including second watermarking by an attacker.Keywords
Wavelets, CNN, Blind Watermarking, Copyright Protection.
References
- Sarita P Ambadekar, Jayshree Jain and Jayshree Khanapuri, “Digital Image Watermarking through Encryption and DWT for Copyright Protection”, Recent Trends in Signal and Image Processing, pp.187-195, 2019.
- Hidangmayum Saxena Devi and Khumanthem Manglem Singh, “Red- Cyan Anaglyph Image Watermarking using DWT, Hadamard Transform and Singular Value Decomposition for Copyright Protection”, Journal of Information Security and Applications, Vol. 50, No. 1, pp. 1-17, 2020.
- Hazem Al-Otum and Nour Emad Al-Shalabi, “Copyright Protection of Color Images for Android-Based Smartphones using Watermarking with Quick-Response Code”, Multimedia Tools and Applications, Vol. 77, pp. 1-24, 2018.
- Radu O Preda and Dragos N Vizireanu, “A Robust Digital Watermarking Scheme for Video Copyright Protection in the Wavelet Domain”, Measurement, Vol. 43, pp. 1720-1726, 2010.
- Radu O Preda and Dragos N Vizireanu, “Robust Wavelet-Based Video Watermarking Scheme for Copyright Protection using the Human Visual System”, Journal of Electronic Imaging, Vol. 20, No. 1, pp. 1-19, 2011.
- Haribabu Kandi, Deepak Mishra and Subrahmanyam R.K. Sai Gorthi, “Exploring the Learning Capabilities of Convolutional Neural Networks for Robust Image Watermarking”, Computers and Security, Vol. 65, pp. 247-268, 2017.
- Seung-Min Mun, Seung-Hun, Jang Nam, Kim Haneol and Heung-Kyu Lee Dongkyu,“ Finding Robust Domain from Attacks: A Learning Framework for Blind Watermarking”, Proceedings of 27th ACM International Conference on Multimedia, pp. 191-202, 2019.
- Jiren Zhu, Russell Kaplan, Justin Johnson and Li Fei-Fei, “Hidden: Hiding data with Deep Networks”. Proceedings of European Conference on Computer Vision, pp. 657-672, 2018.
- Yang Liu, Mengxi Guo, Jian Zhang, Yuesheng Zh and Xiaodong Xie, “A Novel Two-Stage Separable Deep Learning Framework for Practical Blind Watermarking”, Proceedings of ACM International Conference on Multimedia, pp. 1509-1517, 2019.
- Shumeet Baluja,“Hiding Images within Images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 42, No. 7, pp. 1685-1697, 2019.
- Bingyang Wen and Sergul Aydore, “Romark: A Robust Watermarking System using Adversarial Training”, Proceedings of ACM International Workshop on Machine Learning, pp. 1-5, 2019.
- Mahdi Ahmadi, Alireza Norouzi, Nader Karimi and Shadrokh Samavi, “ReDMark: Framework for Residual Diffusion Watermarking based on Deep Networks”, Expert Systems with Applications, Vol. 146, pp. 1131-57, 2020.
- Musrrat Ali and Chang Wook Ahn, “An Optimized Watermarking Technique based on Self-Adaptive DE in DWT-SVD Transform Domain”, Signal Processing, Vol. 94, No. 2, pp. 545-556, 2014.
- Tanya Koohpayeh Araghi, Azizah Abd Manaf and Sagheb Kohpayeh Araghi. “A Secure Blind Discrete Wavelet Transform based Watermarking Scheme using Two-Level Singular Value Decomposition”, Expert Systems with Applications, Vol. 112, pp. 208-228,2018.
- Reem A Alotaibi and Lamiaa A Elrefaei, “Text-Image Watermarking based on Integer Wavelet Transform (IWT) and Discrete Cosine Transform (DCT)”, Applied Computing and Informatics, Vol. 15, No. 1, pp. 191-202, 2018.
- Ladan Salimi, Amir Haghighi and Abdolhossein Fathi, “A novel Watermarking Method based on Differential Evolutionary Algorithm and Wavelet Transform”, Multimedia Tools and Applications, Vol. 81, pp. 1-18, 2020.
- Mohammad Hassan Vali, Ali Aghagolzadeh and Yasser Baleghi. “Optimized Watermarking Technique using Self-Adaptive Differential Evolution based on Redundant Discrete Wavelet Transform and Singular Value Decomposition”, Expert Systems with Applications, Vol. 114, pp. 296-312, 2018.
- Falgun N. Thakkar and Vinay Kumar Srivastava, “A Blind Medical Image Watermarking: DWT-SVD based Robust and Secure Approach for Telemedicine Applications”, Multimedia Tools and Applications, Vol. 76, pp. 3669-3697, 2017.
- Sai Shyam Sharma and V. Chandrasekaran, “A Robust Hybrid Digital Watermarking Technique against a Powerful CNN-based Adversarial attack”, Multimedia Tools and Applications, Vol. 84, pp. 1-22, 2020.
- Break our Steganographic System, Avaialble at http://agents.fel.cvut.cz/boss/index.php?mode=VIEW%20tmpl=materials, Accessed at 2020.
- Zhou Wang, Eero P. Simoncelli and Alan C. Bovik, “Multiscale Structural Similarity for Image Quality Assessment”, Proceedings of 37th Asilomar Conference on Signals, Systems and Computers, pp. 1398-1402, 2003.