摘要
为进一步提高隐写分析模型的检测准确率,提出一种基于注意力机制的浅层图像隐写分析模型,通过使用一个浅层神经网络控制模型参数量和训练时间,引入注意力模块,加速模型收敛,提升模型检测的准确率。实验结果表明,针对3种隐写算法在嵌入率分别为0.2 bpp和0.4 bpp时,检测准确率比浅层卷积神经网络(Shallow Convolution Neural Network,SCNN)均有提升,最高提升5.5%。
In order to further improve the detection accuracy of steganalysis model,a shallow image steganalysis model based on attention mechanism was proposed.By using a shallow neural network to control model parameters and training time,attention module was introduced to accelerate the convergence of the model and improve the detection accuracy of the model.The experimental results show that the detection accuracy of the three steganography algorithms is higher than that of SCNN when the embedding rate is 0.2bpp and 0.4bpp,and the highest is 5.5%.
作者
段明月
李爽
钟小宇
李丽红
DUAN Ming-yue;LI Shuang;ZHONG Xiao-yu;LI Li-hong(College of Science,North China University of Science and Technology,Tangshan Hebei 063210,China;Hebei Province Key Laboratory of Data Science and Application,Tangshan Hebei 063210,China;Tangshan Key Laboratory of Data Science,Tangshan Hebei 063210,China)
出处
《华北理工大学学报（自然科学版）》
CAS
2024年第1期133-140,共8页
Journal of North China University of Science and Technology：Natural Science Edition
基金
河北省数据科学与应用重点实验室项目(10120201)
唐山市数据科学重点实验室项目(10120301)。
关键词
注意力机制
隐写分析
神经网络
attention mechanism
steganalysis
neural network
