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Resampling parameter estimation via dual-filtering based convolutional neural network
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DOI：10.1007/s00530-020-00697-y.png)
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Abstract
En 中文
Resampling detection is an important problem in image forensics. Several exiting approaches have been proposed to solve it, but few of them focus on resampling parameter estimation. Especially, the estimation of downsampling scenarios is very challenging. In this paper, we propose a dual-filtering based convolutional neural network (CNN) to extract features directly from the images. First, we analyze the formulation of resampling parameter estimation and reformulate it as a multi-classification problem by regarding each resampling parameter as a distinct class. Then, we design a network structure based on the preprocessing operation to capture the specific resampling traces for classification. Two parallel filters with different highpass filters are deployed to the CNN architecture, which enlarges the resampling traces and makes it easier to achieve resampling parameter estimation. Next, concatenating the outputs of the two filters by a concat layer. Finally, the experimental results demonstrate our proposed method is effective and has better performance than state-of-the-art methods in resampling parameter estimation.
Keywords:
Image forensics
Resampling detection
Parameter estimation
Convolutional neural network
Dual-filtering
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