{"title":"基于兴趣点的鲁棒盲图像水印技术","authors":"","doi":"10.1016/j.vrih.2023.06.012","DOIUrl":null,"url":null,"abstract":"<div><p>Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability. However, image watermarking algorithms are weak against hybrid attacks, especially geometric at-tacks, such as cropping attacks, rotation attacks, etc. We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further. First, to extract more sparse and stable interest points, we use the Superpoint algorithm for generation and design two steps to perform the screening procedure. We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability. The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework. Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks. We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks. Through extensive experimental results, we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image. Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096579623000426/pdfft?md5=0d46d851b07db92670b0c63431ec427e&pid=1-s2.0-S2096579623000426-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Robust blind image watermarking based on interest points\",\"authors\":\"\",\"doi\":\"10.1016/j.vrih.2023.06.012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability. However, image watermarking algorithms are weak against hybrid attacks, especially geometric at-tacks, such as cropping attacks, rotation attacks, etc. We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further. First, to extract more sparse and stable interest points, we use the Superpoint algorithm for generation and design two steps to perform the screening procedure. We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability. The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework. Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks. We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks. Through extensive experimental results, we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image. Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.</p></div>\",\"PeriodicalId\":33538,\"journal\":{\"name\":\"Virtual Reality Intelligent Hardware\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2096579623000426/pdfft?md5=0d46d851b07db92670b0c63431ec427e&pid=1-s2.0-S2096579623000426-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Virtual Reality Intelligent Hardware\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2096579623000426\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Virtual Reality Intelligent Hardware","FirstCategoryId":"1093","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2096579623000426","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
摘要
数字水印技术在防伪和溯源工作中发挥着至关重要的作用。然而,图像水印算法对混合攻击的抵抗力较弱,尤其是几何攻击,如裁剪攻击、旋转攻击等。我们提出了一种结合稳定兴趣点和深度学习网络的鲁棒盲图像水印算法,以进一步提高水印算法的鲁棒性。首先,为了提取更多稀疏且稳定的兴趣点,我们使用超级点算法进行生成,并设计了两个步骤来执行筛选程序。我们首先保留给定区域内可能性最大的点,以确保点的稀疏性,然后通过混合攻击筛选出稳健的兴趣点,以确保高稳定性。利用基于深度学习的框架,将信息嵌入以稳定兴趣点为中心的子块中。在对抗训练中加入不同类型的攻击和模拟噪声,以保证嵌入块的鲁棒性。我们使用 ConvNext 网络提取水印,并根据未嵌入子块的解码值确定分割阈值。通过大量的实验结果,我们证明了我们提出的算法可以提高网络提取信息的准确性,同时确保嵌入图像与原始覆盖图像之间的高隐蔽性。与之前的 SOTA 工作相比,我们的算法可以在混合攻击和几何攻击中取得更好的视觉和数值结果。
Robust blind image watermarking based on interest points
Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability. However, image watermarking algorithms are weak against hybrid attacks, especially geometric at-tacks, such as cropping attacks, rotation attacks, etc. We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further. First, to extract more sparse and stable interest points, we use the Superpoint algorithm for generation and design two steps to perform the screening procedure. We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability. The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework. Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks. We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks. Through extensive experimental results, we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image. Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.