利用运动向量嵌入概率增强特征的隐写分析方法

Steganalysis Method with Feature Enhanced by Embedding Probability of Motion Vector

文中从隐写方法遵循的最小化失真或最大熵原则出发,将嵌入概率的最优分布作为先验知识来指导视频隐写分析。为更好地刻画运动向量的嵌入优先级,从视频的运动特征、纹理特征以及编码框架下的局部最优性定义失真函数,并利用Gibbs分布估计运动向量的嵌入概率。据此提出一种利用嵌入概率对检测特征进行定量增强的方法,并从相对熵的角度对增强的原理进行了解释。实验结果表明,3种经典的隐写分析算法在使用文中特征增强方法后,检测准确率均有提升,且对码率具有鲁棒性。与新型深度神经网络VSRNet检测方法的对比结果也验证了文中方法的有效性。

Based on the fact that most steganography approaches follow the rules of minimum distortion or maximum entropy,this paper proposed to employ the distribution of optimal embedding probability as the prior knowledge for video steganalysis.To better characterize the embedding priority of motion vectors,it defined a measurement of the embedding distortion of motion vectors using features from three aspects,namely motion feature,texture feature and local optimality under coding framework,and the embedding probabilities of motion vectors were estimated with Gibbs distribution.Thus this study proposed a way of quantitatively enhancing the steganalytic features with the embedding probabilities and the mechanism of the enhancement was explained from the perspective of relative entropy.Experimental results show that the detection accuracy of three classical steganalytic methods have been unanimously improved and show robustness against different bitrates after the enhancement with the proposed method.The effectiveness of the new method is also verified by the comparison with the latest deep neural network VSRNet detection method.