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General High-Pass Convolution: A Novel Convolutional Layer for Image Manipulation Detection

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13534))

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Abstract

Image Manipulation Detection is different from most computer vision tasks, such as Image Classification. The latter pays more attention to image content, while the former focuses more on manipulation trace. However, plain convolutional layer tends to learn features that represent image content rather than features that represent manipulation trace, which degrades the performance of traditional CNNs on Image Manipulation Detection. Inspired by constrained convolutional layer proposed by Bayar et al., we propose General High-Pass Convolution, a new form of convolutional layer which is capable of motivating CNNs to learn manipulation trace features. General High-Pass Convolution is designed to simulate a set of learnable high-pass filters to suppress the image content, thus motivating the CNNs to learn manipulation trace features which are mainly present in the high-frequency components of the image. We conduct comprehensive experiments to evaluate the effectiveness of General High-Pass Convolution. The experimental results show that General High-Pass Convolution achieves better performance than Bayar et al.’s constrained convolutional layer, and can be combined with CNN backbone networks to improve their performance on Image Manipulation Detection, such as VGG and ResNet.

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Notes

  1. 1.

    The code is available at https://github.com/newcomertzc/GHP-Convolution.

References

  1. Acuna, D.E., Brookes, P.S., Kording, K.P.: Bioscience-scale automated detection of figure element reuse, p. 269415. BioRxiv (2018)

    Google Scholar 

  2. Anumala, U., Okade, M.: Forensic detection of median filtering in images using local tetra patterns and J-divergence. In: 2020 National Conference on Communications (NCC), pp. 1–6. IEEE (2020)

    Google Scholar 

  3. Bayar, B., Stamm, M.C.: A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, pp. 5–10 (2016)

    Google Scholar 

  4. Bayar, B., Stamm, M.C.: Constrained convolutional neural networks: a new approach towards general purpose image manipulation detection. IEEE Trans. Inf. Forensics Secur. 13(11), 2691–2706 (2018)

    Article  Google Scholar 

  5. Bik, E.M., Casadevall, A., Fang, F.C.: The prevalence of inappropriate image duplication in biomedical research publications. MBio 7(3), e00809–16 (2016)

    Google Scholar 

  6. Chen, C., Ni, J., Huang, R., Huang, J.: Blind median filtering detection using statistics in difference domain. In: Kirchner, M., Ghosal, D. (eds.) IH 2012. LNCS, vol. 7692, pp. 1–15. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36373-3_1

    Chapter  Google Scholar 

  7. Chen, J., Kang, X., Liu, Y., Wang, Z.J.: Median filtering forensics based on convolutional neural networks. IEEE Sig. Process. Lett. 22(11), 1849–1853 (2015)

    Article  Google Scholar 

  8. Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)

  9. Cozzolino, D., Poggi, G., Verdoliva, L.: Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp. 159–164 (2017)

    Google Scholar 

  10. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  11. Fan, W., Wang, K., Cayre, F.: General-purpose image forensics using patch likelihood under image statistical models. In: 2015 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2015)

    Google Scholar 

  12. Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)

    Article  Google Scholar 

  13. Gallagher, A.C.: Detection of linear and cubic interpolation in JPEG compressed images. In: The 2nd Canadian Conference on Computer and Robot Vision (CRV 2005), pp. 65–72. IEEE (2005)

    Google Scholar 

  14. Goljan, M., Fridrich, J.: CFA-aware features for steganalysis of color images. In: Media Watermarking, Security, and Forensics 2015, vol. 9409, pp. 279–291. SPIE (2015)

    Google Scholar 

  15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  16. Hwang, J.J., Rhee, K.H.: Gaussian filtering detection based on features of residuals in image forensics. In: 2016 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), pp. 153–157. IEEE (2016)

    Google Scholar 

  17. Kang, X., Stamm, M.C., Peng, A., Liu, K.R.: Robust median filtering forensics using an autoregressive model. IEEE Trans. Inf. Forensics Secur. 8(9), 1456–1468 (2013)

    Article  Google Scholar 

  18. Kirchner, M.: Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue. In: Proceedings of the 10th ACM Workshop on Multimedia and Security, pp. 11–20 (2008)

    Google Scholar 

  19. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  20. Liu, L., et al.: On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv:1908.03265 (2019)

  21. Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)

    Google Scholar 

  22. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  23. Mahdian, B., Saic, S.: Blind authentication using periodic properties of interpolation. IEEE Trans. Inf. Forensics Secur. 3(3), 529–538 (2008)

    Article  Google Scholar 

  24. Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)

    Article  Google Scholar 

  25. Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting traces of resampling. IEEE Trans. Sig. Process. 53(2), 758–767 (2005)

    Article  MathSciNet  Google Scholar 

  26. Qiu, X., Li, H., Luo, W., Huang, J.: A universal image forensic strategy based on steganalytic model. In: Proceedings of the 2nd ACM Workshop on Information Hiding and Multimedia Security, pp. 165–170 (2014)

    Google Scholar 

  27. Rahaman, N., et al.: On the spectral bias of neural networks. In: International Conference on Machine Learning, pp. 5301–5310. PMLR (2019)

    Google Scholar 

  28. Rhee, K.H., Chung, I.: Improved feature vector of median filtering residual for image forensics. In: 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS), pp. 1–4. IEEE (2018)

    Google Scholar 

  29. Shi, Y.Q., Sutthiwan, P., Chen, L.: Textural features for steganalysis. In: Kirchner, M., Ghosal, D. (eds.) IH 2012. LNCS, vol. 7692, pp. 63–77. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36373-3_5

    Chapter  Google Scholar 

  30. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  31. Stern, A.M., Casadevall, A., Steen, R.G., Fang, F.C.: Financial costs and personal consequences of research misconduct resulting in retracted publications. Elife 3, e02956 (2014)

    Article  Google Scholar 

  32. Wu, Y., AbdAlmageed, W., Natarajan, P.: ManTra-Net: manipulation tracing network for detection and localization of image forgeries with anomalous features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9543–9552 (2019)

    Google Scholar 

  33. Xu, Z.-Q.J., Zhang, Y., Xiao, Y.: Training behavior of deep neural network in frequency domain. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. LNCS, vol. 11953, pp. 264–274. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36708-4_22

    Chapter  Google Scholar 

  34. Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y.: Large-scale evaluation of splicing localization algorithms for web images. Multimed. Tools Appl. 76(4), 4801–4834 (2016). https://doi.org/10.1007/s11042-016-3795-2

    Article  Google Scholar 

  35. Zhang, Y., Li, S., Wang, S., Shi, Y.Q.: Revealing the traces of median filtering using high-order local ternary patterns. IEEE Sig. Process. Lett. 21(3), 275–279 (2014)

    Article  Google Scholar 

  36. Zhou, P., Han, X., Morariu, V.I., Davis, L.S.: Learning rich features for image manipulation detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1053–1061 (2018)

    Google Scholar 

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Authors and Affiliations

  1. School of Digital Media and Design Arts, Beijing University of Posts and Telecommunications, Beijing, China

    Zecheng Tang & Yang Liu

Authors
  1. Zecheng Tang
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  2. Yang Liu
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Correspondence to Yang Liu .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi’an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Tang, Z., Liu, Y. (2022). General High-Pass Convolution: A Novel Convolutional Layer for Image Manipulation Detection. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_11

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  • DOI: https://doi.org/10.1007/978-3-031-18907-4_11

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