基于自注意力机制的图像隐写分析方法

针对自适应图像隐写分析难度大、现有的模型难以对图像有利区域进行针对性分析的问题,提出了一种基于自注意力机制的图像隐写分析模型(self-attention steganalysis residual network,SA-SRNet)。该模型将自注意力机制引入SRNet(steganalysis residual network),引导模型更加关注图像全局对隐写分析有利的区域及图像长距离之间的依赖关系,解决了硬注意力机制在训练时容易陷入局部最优的问题。首先,奖励机制利用强化学习使模型找到对隐写分析最有利的检测点;其次,自注意力机制根据检测点生成注意力重点图像;最后,替换机制用注意力重点图像替换识别错误的图像,提高训练集的质量和模型的判别能力。实验在BOSSbase 1.01数据集上进行,结果表明SA-SRNet可获得比SRNet更好的隐写分析准确率,最多可提高1.8%。

Image steganalysis based on self-attention

Aiming at the problem that the adaptive steganalysis of images is difficult,and the existing models are difficult to make a targeted analysis of favorable areas of images,this paper proposed an image steganalysis model SA-SRNet based on self-attention.The model introduced self-attention into SRNet to guide the model to focus on favorable areas for steganalysis in the global image and the long-range dependencies of image.It solved the problem that the training of hard-attention was easy to fall into local optimum.First,the reward mechanism used reinforcement learning to guide the model to find the detection point which was most favorable for steganalysis.Secondly,the self-attention used the detection point to generate the important attention image.Finally,the replace mechanism used the important attention images to replace the mis-classified images to improve the quality of training set and the discriminant ability of the model.This paper carried out the experiments on BOSSbase 1.01 dataset.The results show that SA-SRNet can achieve better accuracy of steganalysis than that of SRNet,and can improve the accuracy by up to 1.8%.