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A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics

机译：离散余弦变换域中的深入学习方法到中位过滤取证

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摘要

This letter presents a novel median filtering forensics approach, based on a convolutional neural network (CNN) with an adaptive filtering layer (AFL), which is built in the discrete cosine transform (DCT) domain. Using the proposed AFL, the CNN can determine the main frequency range closely related with the operational traces. Then, to automatically learn the multi-scale manipulation features, a multi-scale convolutional block is developed, exploring a new multi-scale feature fusion strategy based on the maxout function. The resultant features are further processed by a convolutional stream with pooling and batch normalization operations, and finally fed into the classification layer with the Softmax function. Experimental results show that our proposed approach is able to accurately detect the median filtering manipulation and outperforms the state-of-the-art schemes, especially in the scenarios of low image resolution and serious compression loss.
机译：这封信介绍了一种基于卷积神经网络（CNN）的新型中值过滤法，其具有自适应滤波层（AFL），其内置在离散余弦变换（DCT）域中。使用所提出的AFL，CNN可以确定与操作迹线密切相关的主频率范围。然后，为了自动学习多尺度操作特征，开发了一种多尺度卷积块，探索了基于磁峰函数的新的多尺度特征融合策略。由池和批量归一化操作的卷积流进一步处理所得到的功能，最后用SoftMax函数进入分类层。实验结果表明，我们所提出的方法能够准确地检测中值滤波操纵和优于最先进的方案，尤其是在低图像分辨率和严重压缩损耗方面的方案。

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  • 来源
    《IEEE signal processing letters》 |2020年第2020期|276-280|共5页
  • 作者

    Zhang Jun; Liao Yixin; Zhu Xinshan; Wang Hongquan; Ding Jie;

  • 作者单位

    Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China|State Key Lab Digital Publishing Technol Beijing 100871 Peoples R China;

    Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China;

    Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China|State Key Lab Digital Publishing Technol Beijing 100871 Peoples R China;

    Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China;

    Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Convolutional neural networks; discrete cosine transforms; image forensics; median filtering;

    机译：卷积神经网络;离散余弦变换;图像取证;中值过滤;

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