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.

中文翻译：

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基于双滤波的卷积神经网络重采样参数估计

重采样检测是图像取证中的一个重要问题。已经提出了几种现有方法来解决它，但很少有人关注重采样参数估计。特别是，下采样场景的估计非常具有挑战性。在本文中，我们提出了一种基于双重过滤的卷积神经网络 (CNN) 来直接从图像中提取特征。首先，我们分析了重采样参数估计的公式，并通过将每个重采样参数视为一个不同的类来将其重新表述为多分类问题。然后，我们基于预处理操作设计了一个网络结构，以捕获特定的重采样轨迹以进行分类。两个具有不同高通滤波器的并行滤波器被部署到 CNN 架构中，这扩大了重采样轨迹，更容易实现重采样参数估计。接下来，通过“连接”层连接两个过滤器的输出。最后，实验结果表明我们提出的方法是有效的，并且在重采样参数估计方面比最先进的方法具有更好的性能。