空频域结合的多尺度扩张卷积注意力数字水印

Digital Watermarking Combining Spatial Domain and Frequency Domain Based on Multi-scale Expanded Convolutional Attention

目的 将深度学习应用于数字水印,在隐藏信息的同时,不断提高图像的不可见性和鲁棒性,提出一种结合空间域和频率域的多尺度扩张卷积注意力数字水印算法(SF-ACA)。方法 SF-ACA算法的网络框架包含由ACA和SFE构成的生成器、解码器2个部分组成。其中,ACA网络中的MCA模块将3个不同扩张率的扩张卷积对载体图像以多尺度融合的方式进行特征提取,使载体图像能更有效地隐藏水印信息;SFE结合快速傅里叶卷积块,在空域和频域中通过不同大小的感受野捕获互补信息,更精准地获取水印的特征信息,增强了秘密信息的不可见性和鲁棒性。结果 本文提出的水印方法在隐藏与载体图像尺寸相等的三通道彩色图像时,PSNR值为38.81 dB,较UDH方法的PSNR值提高了7.78%。水印图像的隐藏容量是4096比特,该算法与UDH方法在Dropout、Gaussian噪声、JPEG攻击下,提取精度分别提升了5.38%、10.5%、1.65%,满足不可见性要求的同时实现了强鲁棒性。结论 本文方法在隐藏容量较大时,不可见性和鲁棒性都达到了较好的性能。

The work aims to apply the deep learning to the digital watermarking and propose a digital watermarking algorithm combining spatial domain and frequency domain based on multi-scale expanded convolutional attention(SF-ACA),so as to improve the invisibility and robustness of images while concealing information.The network framework of this algorithm consisted of two parts:a generator composed of ACA and SFE and a decoder.Among them,the MCA module in the ACA network combined three dilation convolutions with varying atrous rates for feature extraction of carrier images with multi-scale fusion,so that the carrier images could conceal the watermark information more effectively.The SFE combined fast Fourier convolution blocks to capture complementary information in the spatial and frequency domains with varied widths of perceptual fields to collect the feature information of the watermark more effectively and enhance the invisibility of the secret information and robustness.According to experimental findings,the PSNR value of the proposed watermarking method was 38.81 dB which was improved by 7.78%in comparison to the UDH method while concealing a color image of equal size to the carrier image.The watermarked image had a hiding capacity of 4096 bits,and the method improved the extraction accuracy under Dropout,Gaussian noise,and JPEG attacks by 5.38%,10.5%,and 1.65%,respectively,meeting the requirement of invisibility and achieving strong robustness.When the hiding capacity is high,the method described in this study performs better in terms of robustness and invisibility.