Xception-Based General Forensic Method on Small-Size Images

  • Lisha Yang
  • Pengpeng Yang
  • Rongrong NiEmail author
  • Yao Zhao
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)


Developing universal forensic methods that can simultaneously identify multiple image operations to identify the authenticity and processing history of an image has attracted more and more attention. Although numerous forensics tools and methods emerge to detect the traces left by various image operations, the accuracy with current techniques still decreased significantly as the size of the investigated images reduced. To overcome this issue, especially for small-size or highly compressed images, we propose a method using Xception-based convolution neural network. While CNNs-based methods are able to learn features directly from data for classification task, they are not well suited for forensic problems directly in their original form. Hence, we have added magnified layer in the preprocessing layer. The input images are magnified by the nearest neighbor interpolation algorithm in the magnified layer, which can preserve the property of image operations better than other magnified tools, and then input them into the CNN model for classification. Finally, we get adaptive average pooling function from global average pooling to adapt to any size of input pictures. We evaluate the proposed strategy on six typical image processing operations. Through a series of experiments, we show that this approach can significantly improve classification accuracy to 97.71% when the images are of size 64 × 64. More importantly, it outperforms all the existing general purpose manipulation forensic methods.


Small-size images Operation detection CNN Xception 



This work was supported in part by the National Key Research and Development of China (2018YFC0807306), National NSF of China (61672090, 61532005), and Fundamental Research Funds for the Central Universities (2018JBZ001).


  1. 1.
    Stamm, M.C., Wu, M., Liu, K.J.R.: Information forensics: an overview of the first decade. IEEE Access 1, 167–200 (2013)CrossRefGoogle Scholar
  2. 2.
    Li, H., Luo, W., Qiu, X., Huang, J.: Identification of various image operations using residual-based features. IEEE Trans. Circuits Syst. Video Technol. 1–1 (2016)Google Scholar
  3. 3.
    Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2011)CrossRefGoogle Scholar
  4. 4.
    Bayar, B., Stamm, M.C.: Constrained convolutional neural networks: a new approach towards general purpose image manipulation detection. IEEE Trans. Inf. Forensics Secur. 1–1 (2018)Google Scholar
  5. 5.
    Tang, H., Ni, R., Zhao, Y., Li, X.: Detection of various image operations based on CNN. In: Asia-Pacific Signal and Information Processing Association Summit and Conference, pp. 1479–1485 (2017)Google Scholar
  6. 6.
    Chen, X., Peng, X., Li, J., Peng, Y.: Overview of deep kernel learning based techniques and applications. J. Netw. Intell. 1(3), 83–98 (2016)Google Scholar
  7. 7.
    Xia, Y., Hu, R.: Fuzzy neural network based energy efficiencies control in the heating energy supply system responding to the changes of user demands. J. Netw. Intell. 2(2), 186–194 (2017)MathSciNetGoogle Scholar
  8. 8.
    Chollet, F.: Xception: Deep Learning with Depthwise Separable Convolutions, pp. 1800–1807 (2016)Google Scholar
  9. 9.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)Google Scholar
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition (2015)Google Scholar
  11. 11.
    Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)Google Scholar
  12. 12.
    Tang, H., Ni, R., Zhao, Y., Li, X.: Median filtering detection of small-size image based on CNN. J. Vis. Commun. Image Represent. 51, 162–168 (2018)CrossRefGoogle Scholar
  13. 13.
    Bas, P., Filler, T., Pevný, T.: “Break our steganographic system”: the ins and outs of organizing BOSS. In: International Workshop on Information Hiding, pp. 59–70. Springer, Berlin, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Schaefer, G., Stich, M.: UCID: an uncompressed color image database. In: Storage and Retrieval Methods and Applications for Multimedia, vol. 5307, pp. 472–481. International Society for Optics and Photonics (2004)Google Scholar
  15. 15.
  16. 16.
    Luo, W., Huang, J., Qiu, G.: JPEG error analysis and its applications to digital image forensics. IEEE Trans. Inf. Forensics Secur. 5(3), 480–491 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Lisha Yang
    • 1
    • 2
  • Pengpeng Yang
    • 1
    • 2
  • Rongrong Ni
    • 1
    • 2
    Email author
  • Yao Zhao
    • 1
    • 2
  1. 1.Institute of Information Science, Beijing Jiaotong UniversityBeijingChina
  2. 2.Beijing Key Laboratory of Advanced Information Science and Network TechnologyBeijingChina

Personalised recommendations