In recent years, with the improvements in machine learning, image forensics has made considerable progress in detecting editing manipulations. This progress also raises more questions in image forensics research, such as can the parameters applied in a manipulation be estimated. Many parameter estimation works have already been performed. However, most of these works are based on mathematical analyses. In this paper, we attempt to solve a particular parameter estimation problem from a different aspect. Specifically, a new convolutional neural network (CNN) model is proposed to estimate the resampling rate for resampled images regardless of whether the image is upscaled or downscaled. This model features an original layer to generate a measurable energy map toward the estimation of resampling rate (METEOR). The METEOR layer is demonstrated to be an outstanding method that can assist in enhancing the estimation performance of the CNN. Furthermore, the METEOR layer can also increase the robustness of the CNN against JPEG compression, which makes it extremely important in realistic application scenarios. Our work has verified that machine learning, particularly CNNs, with proper optimization can also be refined to adapt to parameter estimation in digital forensics with excellent performance and robustness.

中文翻译：

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METEOR：通过卷积神经网络估计重采样率的可测量能量图

近年来，随着机器学习的进步，图像取证在检测编辑操作方面取得了长足的进步。这一进展也在图像取证研究中提出了更多问题，例如可以估计应用于操作的参数。许多参数估计工作已经完成。然而，这些工作大部分是基于数学分析。在本文中，我们试图从不同的方面解决一个特定的参数估计问题。具体来说，提出了一种新的卷积神经网络（CNN）模型来估计重采样图像的重采样率，而不管图像是放大还是缩小。该模型具有原始层，可生成可测量的能量图，用于估计重采样率 (METEOR)。METEOR 层被证明是一种出色的方法，可以帮助提高 CNN 的估计性能。此外，METEOR 层还可以增加 CNN 对 JPEG 压缩的鲁棒性，这使得它在现实应用场景中极为重要。我们的工作已经证实，经过适当优化的机器学习，尤其是 CNN，也可以改进以适应具有出色性能和鲁棒性的数字取证中的参数估计。