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Smooth filtering identification based on convolutional neural networks

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Abstract

The increasing prevalence of digital technology brings great convenience to human life, while also shows us the problems and challenges. Relying on easy-to-use image editing tools, some malicious manipulations, such as image forgery, have already threatened the authenticity of information, especially the electronic evidence in the crimes. As a result, digital forensics attracts more and more attention of researchers. Since some general post-operations, like widely used smooth filtering, can affect the reliability of forensic methods in various ways, it is also significant to detect them. Furthermore, the determination of detailed filtering parameters assists to recover the tampering history of an image. To deal with this problem, we propose a new approach based on convolutional neural networks (CNNs). Through adding a transform layer, obtained distinguishable frequency-domain features are put into a conventional CNN model, to identify the template parameters of various types of spatial smooth filtering operations, such as average, Gaussian and median filtering. Experimental results on a composite database show that putting the images directly into the conventional CNN model without transformation can not work well, and our method achieves better performance than some other applicable related methods, especially in the scenarios of small size and JPEG compression.

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Authors and Affiliations

  1. School of Electronic Information Engineering, Tianjin University, Tianjin, China

    Anan Liu, Zhengyu Zhao & Yuting Su

  2. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, 610500, China

    Chengqian Zhang

Authors
  1. Anan Liu
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  2. Zhengyu Zhao
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  3. Chengqian Zhang
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  4. Yuting Su
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Corresponding author

Correspondence to Yuting Su.

Additional information

This work was supported in part by the National Natural Science Foundation of China (61572356, 61472275, 61303208), the Tianjin Research Program of Application Foundation and Advanced Technology (15JCYBJC16200), a grant from the China Scholarship Council (201506255073), and a grant from the Elite Scholar Program of Tianjin University (2014XRG-0046).

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Cite this article

Liu, A., Zhao, Z., Zhang, C. et al. Smooth filtering identification based on convolutional neural networks. Multimed Tools Appl 78, 26851–26865 (2019). https://doi.org/10.1007/s11042-016-4251-z

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  • DOI: https://doi.org/10.1007/s11042-016-4251-z

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