Median filtering detection of small-size image based on CNN

Abstract Existing median filtering detection methods are no longer effective for small size or highly compressed images. To deal with this problem, a new median filtering detection method based on CNN is proposed in this paper. Specifically, a new network structure called MFNet is constructed. First, for preprocessing, the nearest neighbor interpolation method is utilized to up-sample the small-size images. The property of median filtering can be well preserved by the up-sampling operation and enlarged difference between the original image and its median filtered version can be obtained. Then, the well-known mlpconv structure is employed in the first and second layers of MFNet. With mlpconv layers, the nonlinear classification ability of the proposed method can be enhanced. After that, three conventional convolutional layers are utilized to finally derive the feature maps. The experimental results show that the proposed method achieves significant improved detection performance. Moreover, the proposed method performs well for highly compressed image of size as small as 16 × 16.