A Study on the Comparative Analysis of Embedded and Zero Watermarking for Unstructured Image Protection
DOI:
https://doi.org/10.13052/jwe1540-9589.2475Keywords:
watermark, embedded watermark, zero watermark, copyright, image processingAbstract
Embedded watermarking and zero-watermarking, both of which are technologies for copyright protection of digital images, are being actively studied based on their respective advantages and disadvantages. As the prevalence of copyright infringement and the use of irregular-resolution images in game content and media applications increases, there is a growing need for practical and robust image protection techniques. In this paper, we implement discrete wavelet transform (DWT)-discrete cosine transform (DCT)-singular value decomposition (SVD)-based embedded watermarking and hash-based XOR-based zero-watermarking algorithms in Python for about 200 non-standard images with various resolutions and shapes (resolution range: 512 x 512 to 6800 x 4000) and quantitatively compare and analyse their performance. We evaluate the robustness and restoration rate of each watermarking method by applying various attacks such as JPEG compression, blur, Gaussian noise, cropping, and rotation based on peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized correlation (NC) indices. The experimental results show that the embedded method showed fast processing speed and stable quality maintenance performance even in high-resolution images, and zero watermarking had the advantage of not damaging the original image, but was relatively prone to being affected by restoration sensitivity and execution time. While not directly implemented in this study, the findings provide a foundational reference for integrating robust watermarking mechanisms into blockchain and InterPlanetary File System (IPFS) based copyright authentication systems, particularly for protecting high-resolution or irregularly shaped visual assets.
Downloads
References
Varghese, J., Bin Hussain, O., Subash, S., and Abdul Razak, T. (2023). An effective digital image watermarking scheme incorporating DCT, DFT and SVD transformations. PeerJ Computer Science.
Rahman, M. M. (2013). A DWT, DCT and SVD Based Watermarking Technique to Protect the Image Piracy. International Journal of Managing Public Sector Information and Communication Technologies, 4(2), 21–32.
Liu, Y., Li, Y., Wang, Z., and Li, S. (2021). Image copyright protection based on blockchain and zero-watermark. Journal of Intelligent & Fuzzy Systems, 41(1), 2021–2032.
Zhang, Y., Liu, S., and Wang, Q. (2020). A zero-watermark algorithm for copyright protection of remote sensing image based on blockchain. Computers, Materials & Continua, 65(3), 2345–2359.
Khan, M. I., Rahman, M. M., and Sarker, M. I. H. (2013). Digital Watermarking for Image Authentication Based on Combined DCT, DWT and SVD Transformation. International Journal of Computer Applications, 72(20), 1–6.
Roy, R. (2012). Robust Image Watermarking based on DCT-DWT-SVD Method. International Journal of Computer Applications, 58(21), 1–6.
Lai, C.-C., and Tsai, C.-C. (2010). Digital image watermarking using discrete wavelet transform and singular value decomposition. IEEE Transactions on Instrumentation and Measurement, 59(11), 3060–3063.
Bhatnagar, G., and Raman, B. (2009). A new robust reference watermarking scheme based on DWT-SVD. Computer Standards & Interfaces, 31(5), 1002–1013.
Thakkar, F. N., and Srivastava, V. K. (2017). A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications. Multimedia Tools and Applications, 76(3), 3669–3697.
Priyanka, Kumar, P., and Tewari, R. G. (2017). Security of Medical Images by Watermarking using DWT-DCT-SVD. International Journal of Engineering Research & Technology (IJERT), 6(4), 467–470.
Liu, Y., Li, H., and Zhang, Y. (2024). ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with Robustness and Security. Applied Sciences, 14(1), 435.
Xu, H., et al. (2023). Zero-Watermark Scheme for Medical Image Protection Based on Style Feature and ResNet. Biomedical Signal Processing and Control, 86, 104810.
Wang, R., et al. (2020). A Novel Zero-Watermarking Scheme Based on Variable Parameter Chaotic Mapping in NSPD-DCT Domain. IEEE Access, 8, 182391–182411.
Xu, H. (2021). Digital Media Zero Watermark Copyright Protection Algorithm Based on Embedded Intelligent Edge Computing Detection. Mathematical Biosciences and Engineering, 18(5), 6771–6789.
Amiri, R., and Mirzakuchaki, S. (2022). A novel zero-watermarking scheme based on NSCT-SVD and blockchain for video copyright. EURASIP Journal on Wireless Communications and Networking, 2022(1), 20.
Han, B., et al. (2021). Zero-watermarking algorithm for medical image based on VGG19 deep convolution neural network. Journal of Healthcare Engineering, 2021, 1–10.
Arévalo-Ancona, R. E., et al. (2024). Secure Medical Image Authentication Using Zero-Watermarking Based on Deep Learning Context Encoder. Computación y Sistemas, 28(1), 1–10.
Ganguly, K. (2017). Learning Generative Adversarial Networks: Next Generation Deep Learning Simplified. Packt Publishing.
Dong, P., et al. (2005). Digital watermarking robust to geometric distortions. IEEE Transactions on Image Processing, 14(12), 2140–2150.
Yang, C., et al. (2021). Robust zero watermarking algorithm for medical images based on Zernike-DCT. Security and Communication Networks, 2021, 1–10.
Wang, S., et al. (2019). Robust blind watermarking against Gaussian noise. Multimedia Tools and Applications, 78(18), 25745–25766.
Barr, J., et al. (2003). Using Digital Watermarks with Image Signatures to Mitigate the Threat of the Copy Attack. Proceedings of ICASSP, 2003, 1–4.
Kutter, M., et al. (2000). The Watermark Copy Attack. Proceedings of the SPIE, 3971, 371–380.
Feizi, S., et al. (2023). Researchers Tested AI Watermarks – and Broke All of Them. Wired.
WAVES: Benchmarking the Robustness of Image Watermarks. (2024). arXiv preprint arXiv:2401.08573.
Deguillaume, F., et al. (2003). Secure hybrid robust watermarking resistant against tampering and copy attack. Signal Processing, 83(10), 2133–2170.
Kim, Y., Kim, S.-Y., Kamyod, C., and Park, B. (2023). Proposition of robustness indicators for immersive content filtering. Journal of Web Engineering, 22(4), 731–756.
Agarwal, H., and Chandel, G. S. (2022). Design of a Hybrid Digital Watermarking Algorithm with High Robustness. Journal of Web Engineering, 21(7), 2243–2261.
