Abstract
This paper studies the capability of non-salient image regions and pattern selection for advancing the security robustness of region-based watermark. However, considerable unpredictability in the assigning image region for encryption exists, since saliency map itself is changed specifically by particular encryption algorithm and saliency detection techniques. The change may lead to misguided selection of image region for decryption. A new solution for watermarking in non-salient region is described and evaluated to derive improvement of secrecy, integrity, and availability of images. This is achieved by using machine learning approach, which favors the advantageous selection of encryption region based on the variation between the salient maps before and after the encryption process. We validate the method by region selection evaluation and the correctness of decrypted messages. Experimental results show that a range of saliency models are adequate for the proposed watermark solution.
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Anh, D.N., Huy, P.Q., Mai, L.C. (2020). Watermark by Learning Non-saliency. In: Satapathy, S., Bhateja, V., Nguyen, B., Nguyen, N., Le, DN. (eds) Frontiers in Intelligent Computing: Theory and Applications. Advances in Intelligent Systems and Computing, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-32-9186-7_7
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DOI: https://doi.org/10.1007/978-981-32-9186-7_7
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