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Image source identification with known post-processed based on convolutional neural network
Signal Processing: Image Communication ( IF 3.5 ) Pub Date : 2021-08-17 , DOI: 10.1016/j.image.2021.116438
Xin Liao 1, 2 , Jing Chen 1 , Jiaxin Chen 1
Affiliation  

Image source identification is important to verify the origin and authenticity of digital images. However, when images are altered by some post-processing, the performance of the existing source verification methods may degrade. In this paper, we propose a convolutional neural network (CNN) to solve the above problem. Specifically, we present a theoretical framework for different tampering operations, to confirm whether a single operation has affected photo response non-uniformity (PRNU) contained in images. Then, we divide these operations into two categories: non-influential operation and influential operation. Besides, the images altered by the combination of non-influential and influential operations are equal to images that have only undergone a single influential operation. To make our introduced CNN robust to both non-influential operation and influential operation, we define a multi-kernel noise extractor that consists of a high-pass filter and three parallel convolution filters of different sizes. The features generated by the parallel convolution layers are then fed to subsequent convolutional layers for further feature extraction. The experimental results provide the effectiveness of our method.



中文翻译:

基于卷积神经网络的已知后处理图像源识别

图像来源识别对于验证数字图像的来源和真实性很重要。然而,当图像被一些后处理改变时,现有源验证方法的性能可能会下降。在本文中,我们提出了一个卷积神经网络(CNN)来解决上述问题。具体来说,我们提出了不同篡改操作的理论框架,以确认单个操作是否影响了图像中包含的光响应非均匀性(PRNU)。然后,我们将这些操作分为两类:无影响操作和有影响操作。此外,通过无影响和有影响的操作组合改变的图像等于只进行了一次有影响的操作的图像。为了使我们引入的 CNN 对非影响操作和影响操作都具有鲁棒性,我们定义了一个多核噪声提取器,它由一个高通滤波器和三个不同大小的并行卷积滤波器组成。然后将并行卷积层生成的特征馈送到后续卷积层以进行进一步的特征提取。实验结果证明了我们方法的有效性。

更新日期:2021-08-27
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