A novel multiple watermarks cooperative authentication algorithm was presented for image contents authentication.This algorithm is able to extract multiple features from the image wavelet domain,which is based on that...A novel multiple watermarks cooperative authentication algorithm was presented for image contents authentication.This algorithm is able to extract multiple features from the image wavelet domain,which is based on that the t watermarks are generated.Moreover,a new watermark embedding method,using the space geometric model,was proposed,in order to effectively tackle with the mutual influences problem among t watermarks.Specifically,the incidental tampering location,the classification of intentional content tampering and the incidental modification can be achieved via mutual cooperation of the t watermarks.Both the theoretical analysis and simulations results validate the feasibility and efficacy of the proposed algorithm.展开更多
针对基于深度学习的水印方法未充分突显图像的关键特征,以及未有效利用中间卷积层输出特征的问题,为提升含水印图像的视觉质量和抵抗噪声攻击的能力,提出一种融合注意力机制和多尺度特征的图像水印方法。在编码器部分,设计注意力模块关...针对基于深度学习的水印方法未充分突显图像的关键特征,以及未有效利用中间卷积层输出特征的问题,为提升含水印图像的视觉质量和抵抗噪声攻击的能力,提出一种融合注意力机制和多尺度特征的图像水印方法。在编码器部分,设计注意力模块关注重要图像特征,以减小水印嵌入引起的图像失真;在解码器部分,设计多尺度特征提取模块,以捕获不同层次的图像细节。实验结果表明,在COCO数据集上与深度水印模型HiDDeN(Hiding Data with Deep Networks)相比,所提方法生成的含水印图像的峰值信噪比(PSNR)和结构相似度(SSIM)分别增加了11.63%和1.29%;所提方法针对dropout、cropout、crop、高斯模糊和JPEG压缩的水印提取平均误比特率(BER)降低了53.85%;此外,消融实验结果验证了添加注意力模块和多尺度特征提取模块的方法有更好的不可见性和鲁棒性。展开更多
基金Project(2012BAH09B02) supported by the National Science and Technology Support Program,ChinaProjects(12JJ3068,12JJ2041) supported by the Natural Science Fundation of Hunan Province,China
文摘A novel multiple watermarks cooperative authentication algorithm was presented for image contents authentication.This algorithm is able to extract multiple features from the image wavelet domain,which is based on that the t watermarks are generated.Moreover,a new watermark embedding method,using the space geometric model,was proposed,in order to effectively tackle with the mutual influences problem among t watermarks.Specifically,the incidental tampering location,the classification of intentional content tampering and the incidental modification can be achieved via mutual cooperation of the t watermarks.Both the theoretical analysis and simulations results validate the feasibility and efficacy of the proposed algorithm.
文摘针对基于深度学习的水印方法未充分突显图像的关键特征,以及未有效利用中间卷积层输出特征的问题,为提升含水印图像的视觉质量和抵抗噪声攻击的能力,提出一种融合注意力机制和多尺度特征的图像水印方法。在编码器部分,设计注意力模块关注重要图像特征,以减小水印嵌入引起的图像失真;在解码器部分,设计多尺度特征提取模块,以捕获不同层次的图像细节。实验结果表明,在COCO数据集上与深度水印模型HiDDeN(Hiding Data with Deep Networks)相比,所提方法生成的含水印图像的峰值信噪比(PSNR)和结构相似度(SSIM)分别增加了11.63%和1.29%;所提方法针对dropout、cropout、crop、高斯模糊和JPEG压缩的水印提取平均误比特率(BER)降低了53.85%;此外,消融实验结果验证了添加注意力模块和多尺度特征提取模块的方法有更好的不可见性和鲁棒性。
