CAJ | 학술논문

为提高基于机器学习的数字水印算法的鲁棒性与不可感知性，根据支持向量机在有限训练样本的情况下具有很好的学习和泛化能力，图像的不同区域邻域像素与中心像素的关系紧密程度不同，文章提出一种基于区域性构建支持向量机模型与Arnold变换相结合的空域水印算法。利用不同区域的邻域像素与中心像素的不同关系紧密程度构建不同区域，从而构建不同的支持向量机模型，并通过水印的Arnold变换预处理实现水印的随机嵌入和提取操作。实验证明，该算法在剪切攻击、椒盐攻击、对比度增强方面相对其他基于机器学习的水印算法有良好的改善，并具有良好的不可感知性。
위제고기우궤기학습적수자수인산법적로봉성여불가감지성，근거지지향량궤재유한훈련양본적정황하구유흔호적학습화범화능력，도상적불동구역린역상소여중심상소적관계긴밀정도불동，문장제출일충기우구역성구건지지향량궤모형여Arnold변환상결합적공역수인산법。이용불동구역적린역상소여중심상소적불동관계긴밀정도구건불동구역，종이구건불동적지지향량궤모형，병통과수인적Arnold변환예처리실현수인적수궤감입화제취조작。실험증명，해산법재전절공격、초염공격、대비도증강방면상대기타기우궤기학습적수인산법유량호적개선，병구유량호적불가감지성。
In order to improve the robustness and imperceptibility of digital watermarking based on machine learning, considering the good learning ability and generalization ability of Support Vecor Machine(SVM) with limited training samples, and different relationship between the center pixel and neighborhood pixels in different areas, a new spatial digital image watermarking based on regional SVM model is proposed, which embed the watermarking based on Arnold transform. Due to the different degrees of releationship between the center pixel and neighborhood pixels, different kind of areas are built. SVM model is built in every kind of area, and then watermarking generated by Arnold transform is embedded and extracted. Experiments showed that the proposed scheme had good imperceptibility and better robustness against several attacks this algorithm attacks, such as cutting, pepper&salt, contrast enhancement and so on, than other watermarking algorithm based on machine learning.