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首页> 外文会议>SPIE Medical Imaging Conference >Discriminative and robust zero-watermarking scheme based on completed local binary pattern for authentication and copyright identification of medical images
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Discriminative and robust zero-watermarking scheme based on completed local binary pattern for authentication and copyright identification of medical images

机译：基于完整局部二进制模式的鉴别鲁棒零水印方案，用于医学图像的认证和版权识别

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Authentication and copyright identification are two critical security issues for medical images. Although zero-watermarking schemes can provide durable, reliable and distortion-free protection for medical images, the existing zero-watermarking schemes for medical images still face two problems. On one hand, they rarely considered the distinguishability for medical images, which is critical because different medical images are sometimes similar to each other. On the other hand, their robustness against geometric attacks, such as cropping, rotation and flipping, is insufficient. In this study, a novel discriminative and robust zero-watermarking (DRZW) is proposed to address these two problems. In DRZW, content-based features of medical images are first extracted based on completed local binary pattern (CLBP) operator to ensure the distinguishability and robustness, especially against geometric attacks. Then, master shares and ownership shares are generated from the content-based features and watermark according to (2,2) visual cryptography. Finally, the ownership shares are stored for authentication and copyright identification. For queried medical images, their content-based features are extracted and master shares are generated. Their watermarks for authentication and copyright identification are recovered by stacking the generated master shares and stored ownership shares. 200 different medical images of 5 types are collected as the testing data and our experimental results demonstrate that DRZW ensures both the accuracy and reliability of authentication and copyright identification. When fixing the false positive rate to 1.00%, the average value of false negative rates by using DRZW is only 1.75% under 20 common attacks with different parameters.
机译：身份验证和版权标识是医学图像的两个关键安全问题。尽管零水印方案可以为医学图像提供持久，可靠和无失真的保护，但是现有的医学图像零水印方案仍然面临两个问题。一方面，他们很少考虑医学图像的可区分性，这是至关重要的，因为不同的医学图像有时彼此相似。另一方面，它们对诸如裁剪，旋转和翻转之类的几何攻击的鲁棒性不足。在这项研究中，提出了一种新颖的区分性和鲁棒性零水印（DRZW）来解决这两个问题。在DRZW中，首先基于完整的本地二进制模式（CLBP）运算符提取医学图像的基于内容的特征，以确保可区分性和鲁棒性，尤其是针对几何攻击。然后，根据（2,2）视觉加密技术，从基于内容的特征和水印生成主份额和所有权份额。最后，所有权份额被存储以用于认证和版权识别。对于查询的医学图像，将提取其基于内容的特征并生成主共享。通过堆叠生成的主共享和存储的所有权共享，可以恢复用于身份验证和版权识别的水印。收集了5种类型的200张不同的医学图像作为测试数据，我们的实验结果表明DRZW确保了身份验证和版权识别的准确性和可靠性。将误报率固定为1.00％时，在20种不同参数的常见攻击下，使用DRZW的误报率平均值仅为1.75％。

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