A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism  

作　　者：Nannan Wu Xianyi Chen James Msughter Adeke Junjie Zhao 

机构地区：[1]School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing,210044,China [2]School of Electronics and Information Engineering,Nanjing University of Information Science and Technology,Nanjing,210044,China

出　　处：《Computers, Materials & Continua》2024年第3期3001-3019,共19页计算机、材料和连续体（英文）

基　　金：supported by the National Key R&D Program of China(Grant Number 2021YFB2700900);the National Natural Science Foundation of China(Grant Numbers 62172232,62172233);the Jiangsu Basic Research Program Natural Science Foundation(Grant Number BK20200039).

摘　　要：Recently,deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding.However,these approaches have some limitations.For example,a cover image lacks self-adaptability,information leakage,or weak concealment.To address these issues,this study proposes a universal and adaptable image-hiding method.First,a domain attention mechanism is designed by combining the Atrous convolution,which makes better use of the relationship between the secret image domain and the cover image domain.Second,to improve perceived human similarity,perceptual loss is incorporated into the training process.The experimental results are promising,with the proposed method achieving an average pixel discrepancy(APD)of 1.83 and a peak signal-to-noise ratio(PSNR)value of 40.72 dB between the cover and stego images,indicative of its high-quality output.Furthermore,the structural similarity index measure(SSIM)reaches 0.985 while the learned perceptual image patch similarity(LPIPS)remarkably registers at 0.0001.Moreover,self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces,making it suitable for diverse computer vision tasks.

关 键 词：Deep image hiding attention mechanism privacy protection data security visual quality 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]