A zero-watermark scheme with geometrical invariants using SVM and PSO against geometrical attacks for image protection
A zero-watermark scheme with geometrical invariants using SVM and PSO against geometrical attacks for image protection
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DOI：
10.1016/j.jss.2012.08.040
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发表时间：
2013-02
期刊：
影响因子：
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通讯作者：
Hung-Hsu Tsai;Yen-Shou Lai;Shih-Che Lo
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文献类型：
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作者：
Hung-Hsu Tsai;Yen-Shou Lai;Shih-Che Lo
提出了一种基于支持向量机分类器的几何不变量零水印图像认证方案。在这里，几何攻击仅仅针对图像上的旋转、缩放和平移（translation，缩写为RST）操作。该方案被称为基于支持向量机的零水印（SZW）计划以下。SZW方法在不改变原始图像的同时嵌入图像的所有者签名，从而实现高透明度。此外，为了提高图像的鲁棒性，该算法将离散傅里叶变换（DFT）与对数极坐标映射（LPM）相结合，用于求取图像的非线性不变量。然后，SZW方法通过对原始水印和宿主图像的不变量的一组特征执行逻辑运算异或（XOR）操作来生成宿主图像的密钥。随后，一个训练的SVM（TSVM）被视为一个映射，使它可以记住的特征集的不变量和密钥之间的关系。在SZW方法的水印提取过程中，首先将水印图像的不变量特征集输入TSVM，得到估计的密钥。然后，SZW方法提取估计的水印通过执行XOR运算的特征的集合上的不变量和估计的秘密密钥。因此，SZW方法在提取水印时不需要原始图像。在本文中，粒子群优化（PSO）算法也被用来搜索一组接近最优的支持向量机的参数。最后，实验结果表明，在平均情况下，SZW方法优于其他现有的方法，在这里考虑的恶意攻击。
This paper proposes a zero-watermark scheme with geometrical invariants using support vector machine (SVM) classifier against geometrical attacks for image authentication. Here geometrical attacks merely address rotation, scale, and translation (RST) operations on images. The proposed scheme is called the SVM-based zero-watermark (SZW) scheme hereafter. The SZW method makes no changes to original images while embedding the owner signature of images so as to achieve high transparency. Moreover, in order to promote the robustness to RST operations, it integrates the discrete Fourier transform (DFT) with the log-polar mapping (LPM) for finding out RST invariants of images. The SZW method then generates the secret key for a host image via performing a logical operation exclusive disjunction, an exclusive-or (XOR) operation, on the original watermark and a set of the characteristics of the RST invariants of the host image. Subsequently, a trained SVM (TSVM) is regarded as a mapping so that it can memorize the relationships between the set of characteristics of RST invariants and the secret key. During the watermark-extraction process of the SZW method, the TSVM is first fed with the set of characteristics of RST invariants of the watermarked image to get the estimated secret key. The SZW method then extracts the estimated watermark by performing the XOR operation on the set of characteristics of RST invariants and the estimated secret key. Consequently, the SZW method requires no original image while retrieving watermarks. In the paper, the particle swarm optimization (PSO) algorithm is also employed to search for a set of nearly optimal parameters of the SVM. Finally, the experimental results show that, in average, the SZW method outperforms other existing methods against RST attacks under consideration here.