Abstract
With the digitalization of the physical markets, the number of users engaging in e-commerce and shopping online is rapidly increasing. An important application area for digital watermarking is the product tracking scenario. For product tracking scenario, watermarking can be used to provide both product links and copyright protection, so the robustness and extraction efficiency of watermarking are the most important metrics. The auto-convolution function (ACNF) based watermarking scheme is the latest image watermarking that achieves the most comprehensive robustness. However, ACNF watermarking is not resilient in the case of Gauss noise and average filtering. Besides, ACNF watermarking focuses only on robustness and ignores extraction efficiency, and the low efficiency of watermark extraction leads to unpleasant user experience. In this paper, we propose an adaptive despread spectrum-based image watermarking for fast product tracking. For watermark embedding, we construct a low-frequency watermark signal in the spatial domain to enhance the robustness to signal processing attacks. In watermark extraction, our scheme uses discrete wavelet transform (DWT) for image dimensionality reduction and adaptively watermark despread spectrum according to the wavelet decomposition level, which can achieve accurate and fast extraction of watermark. The experimental results demonstrate that our proposed watermarking scheme has superior robustness and extraction efficiency than the existing methods under the same imperceptibility.
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Acknowledegment
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 62272331 and 61972269, and Sichuan Science and Technology Program under Grant 2022YFG0320.
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Zhang, F., Wang, H., He, M., Li, J. (2023). Adaptive Despread Spectrum-Based Image Watermarking for Fast Product Tracking. In: Zhao, X., Tang, Z., Comesaña-Alfaro, P., Piva, A. (eds) Digital Forensics and Watermarking. IWDW 2022. Lecture Notes in Computer Science, vol 13825. Springer, Cham. https://doi.org/10.1007/978-3-031-25115-3_12
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DOI: https://doi.org/10.1007/978-3-031-25115-3_12
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