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On-line Access: 2024-03-25

Received: 2022-12-08

Revision Accepted: 2024-03-25

Crosschecked: 2023-03-25

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Chengmeng LIU


Zhi LI


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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.3 P.384-397


A robust tensor watermarking algorithm for diffusion-tensor images

Author(s):  Chengmeng LIU, Zhi LI, Guomei WANG, Long ZHENG

Affiliation(s):  State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China

Corresponding email(s):   62377400@qq.com, zhili@gzu.edu.cn, 306252084@qq.com, zhenglong178@163.com

Key Words:  Robust watermarking algorithm, Transformer, Image reconstruction, Diffusion tensor images, Soft attention, Hard attention, T1-weighted images

Chengmeng LIU, Zhi LI, Guomei WANG, Long ZHENG. A robust tensor watermarking algorithm for diffusion-tensor images[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(3): 384-397.

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T1 - A robust tensor watermarking algorithm for diffusion-tensor images
A1 - Chengmeng LIU
A1 - Zhi LI
A1 - Guomei WANG
A1 - Long ZHENG
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DOI - 10.1631/FITEE.2200628

Watermarking algorithms that use convolution neural networks have exhibited good robustness in studies of deep learning networks. However, after embedding watermark signals by convolution, the feature fusion efficiency of convolution is relatively low; this can easily lead to distortion in the embedded image. When distortion occurs in medical images, especially in diffusion tensor images (DTIs), the clinical value of the DTI is lost. To address this issue, a robust watermarking algorithm for DTIs implemented by fusing convolution with a transformer is proposed to ensure the robustness of the watermark and the consistency of sampling distance, which enhances the quality of the reconstructed image of the watermarked DTIs after embedding the watermark signals. In the watermark-embedding network, T1-weighted (T1w) images are used as prior knowledge. The correlation between T1w images and the original DTI is proposed to calculate the most significant features from the T1w images by using the transformer mechanism. The maximum of the correlation is used as the most significant feature weight to improve the quality of the reconstructed DTI. In the watermark extraction network, the most significant watermark features from the watermarked DTI are adequately learned by the transformer to robustly extract the watermark signals from the watermark features. Experimental results show that the average peak signal-to-noise ratio of the watermarked DTI reaches 50.47 dB, the diffusion characteristics such as mean diffusivity and fractional anisotropy remain unchanged, and the main axis deflection angle αAC is close to 1. Our proposed algorithm can effectively protect the copyright of the DTI and barely affects the clinical diagnosis.


摘要:在深度学习网络的研究中,使用卷积神经网络的水印算法表现出良好的鲁棒性。然而,通过卷积嵌入水印信号后,卷积的特征融合效率相对较低;这很容易导致嵌入图像的失真。当医学图像发生失真时,特别是在扩散张量图像(DTI)中,DTI的临床价值就会丧失。为解决这个问题,提出一种通过融合卷积与Transformer实现的DTI鲁棒性水印算法,以确保水印的鲁棒性和采样距离的一致性,从而提高嵌入水印信号后的DTI重建图像质量。在水印嵌入网络中,使用T1加权(T1w)图像作为先验知识。提出T1w图像和原始DTI之间的相关性,并利用Transformer从T1w图像中提取与原始DTI最相关的重要特征提升重建DTI图像质量。在水印提取网络中,Transformer充分学习水印DTI中最重要的水印特征,从而从水印特征中鲁棒提取水印信号。实验结果表明,水印DTI的平均峰值信噪比(PSNR)达到50.47 dB,扩散特征如平均扩散率和各向异性分数保持不变,主轴偏转角αAC接近1。所提算法可以有效保护DTI版权,几乎不影响临床诊断。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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