传感器模式噪声SPN (sensor pattern noise) 的提取是图像源辨识的关键环节。由于传统方法提取的SPN受场景污迹干扰严重,为此提出一种基于双域联合滤波的SPN提取方法。利用正交小波变换的去相关性，在系数的细节及近似子带分别应用局部自适应MMSE （最小均方误差）滤波与边界保护特性的双边滤波，在空域进行双边滤波；然后用9台相机的参考SPN构造基于相关性检测原理的分类器，将提取的被检图像局部区域SPN输入分类器实现类别辨识；最后重点分析了基于3种典型局部区域SPN的图像源辨识情况。针对局部区域图像的实验结果表明，该方法能有效降低过多场景污迹对SPN的干扰，即使局部区域为256×256像素时，仍能获得79.32%的辨识精度。

The extraction of SPN (sensor pattern noise) is a key procedure for images source identification. But the SPN extracted by existing methods are susceptible to the interference of texture and scene in images. For these reasons, a novel method is proposed for SPN extraction based on combined filtering. Firstly, images are analyzed with an orthogonal wavelet transform, an edge preserving bilateral filter and a local adaptive minimum mean squared error filter for the approximation and detail subband respectively. Then we denoised with bilateral filter in spatial domain. Finally, the classifier based on correlation detection principle is constructed with 9 cameras reference SPN, and accurate identification for SPN has been achieved. Then we analyzed the results of 3 kinds of typical size. The experimental results indicate that the proposed method effectively reduces the interference due to scene. A 79.32% accuracy has been achieved even the size of detected images are 256×256 pixels.