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U Can’t (re)Touch This – A Deep Learning Approach for Detecting Image Retouching

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Image Analysis and Processing – ICIAP 2022 (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13232))

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Abstract

Mobile applications make it easier than ever to edit images and share these images on the Internet. Even if this image manipulation is not a criminal offense in most cases, retouched images can have a number of negative effects.

The goal of this work is to provide a solution for an automatic recognition of image retouching. For this, we adapt a well known convolutional neural network (CNN) from [5] to the domain of RGB images and create a data set to train it. Specifically, we process 1 000 images with nine different filters using Snapseed, an image editing app. Then, we use these 10 000 images in two different experiments to train the adapted CNN. The first experiment compares each single filter with the original images, while the second experiment tries to distinguish all ten classes at once.

Overall, the first experiment achieves an accuracy of over 99% for most classes, and the second experiments a total accuracy of roughly 88%. Finally, we analyze, why some of the classes have lower accuracies.

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Notes

  1. 1.

    https://www.bbc.com/news/newsbeat-57721080 [Last accessed: April 16, 2022].

  2. 2.

    https://www.instagram.com/.

  3. 3.

    https://snapseed.online.

  4. 4.

    https://pixlr.com/.

  5. 5.

    https://github.com/DanielAumayr/U-Can-t-re-touch-this.

  6. 6.

    https://www.bluestacks.com/de/index.html.

References

  1. Agung, N.F.A., Darma, G.: Opportunities and challenges of Instagram algorithm in improving competitive advantage. Int. J. Innov. Sci. Res. Technol. 4(1), 743–747 (2019)

    Google Scholar 

  2. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Del Tongo, L., Serra, G.: Copy-move forgery detection and localization by means of robust clustering with J-Linkage. Signal Process. Image Commun. 28(6), 659–669 (2013)

    Article  Google Scholar 

  3. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G.: A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)

    Article  Google Scholar 

  4. Amerini, I., Uricchio, T., Ballan, L., Caldelli, R.: Localization of JPEG double compression through multi-domain convolutional neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1865–1871. IEEE (2017)

    Google Scholar 

  5. 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 

  6. Bayar, B., Stamm, M.C.: Design principles of convolutional neural networks for multimedia forensics. Electron. Imaging 2017(7), 77–86 (2017)

    Article  Google Scholar 

  7. 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 

  8. Böhme, R., Freiling, F.C., Gloe, T., Kirchner, M.: Multimedia forensics is not computer forensics. In: Geradts, Z.J.M.H., Franke, K.Y., Veenman, C.J. (eds.) IWCF 2009. LNCS, vol. 5718, pp. 90–103. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03521-0_9

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  10. Cox, I., Miller, M., Bloom, J., Fridrich, J., Kalker, T.: Digital Watermarking and Steganography. Morgan kaufmann (2007)

    Google Scholar 

  11. Cozzolino, D., Poggi, G., Verdoliva, L.: Splicebuster: A new blind image splicing detector. In: 2015 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2015)

    Google Scholar 

  12. Cozzolino, D., Verdoliva, L.: Camera-based image forgery localization using convolutional neural networks. In: 2018 26th European Signal Processing Conference (EUSIPCO), pp. 1372–1376. IEEE (2018)

    Google Scholar 

  13. Dang-Nguyen, D.T., Pasquini, C., Conotter, V., Boato, G.: RAISE: a raw images dataset for digital image forensics. In: Proceedings of the 6th ACM Multimedia Systems Conference, pp. 219–224 (2015)

    Google Scholar 

  14. Ferrara, P., Bianchi, T., De Rosa, A., Piva, A.: Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans. Inf. Forensics Secur. 7(5), 1566–1577 (2012)

    Article  Google Scholar 

  15. Kirchner, M., Böhme, R.: Hiding traces of resampling in digital images. IEEE Trans. Inf. Forensics Secur. 3(4), 582–592 (2008)

    Article  Google Scholar 

  16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  17. Lukáš, J., Fridrich, J., Goljan, M.: Detecting digital image forgeries using sensor pattern noise. In: Security, Steganography, and Watermarking of Multimedia Contents VIII, vol. 6072, p. 60720Y. International Society for Optics and Photonics (2006)

    Google Scholar 

  18. Lup, K., Trub, L., Rosenthal, L.: Instagram# Instasad?: exploring associations among Instagram use, depressive symptoms, negative social comparison, and strangers followed. Cyberpsychol. Behav. Soc. Netw. 18(5), 247–252 (2015)

    Article  Google Scholar 

  19. Marra, F., Gragnaniello, D., Verdoliva, L., Poggi, G.: A full-image full-resolution end-to-end-trainable CNN framework for image forgery detection. IEEE Access 8, 133488–133502 (2020)

    Article  Google Scholar 

  20. Meena, K.B., Tyagi, V.: Image forgery detection: survey and future directions. In: Shukla, R.K., Agrawal, J., Sharma, S., Singh Tomer, G. (eds.) Data, Engineering and Applications, pp. 163–194. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6351-1_14

    Chapter  Google Scholar 

  21. Pasquini, C., Amerini, I., Boato, G.: Media forensics on social media platforms: a survey. EURASIP J. Inf. Secur. 2021(1), 1–19 (2021). https://doi.org/10.1186/s13635-021-00117-2

    Article  Google Scholar 

  22. Pasquini, C., Boato, G., Pérez-González, F.: Statistical detection of JPEG traces in digital images in uncompressed formats. IEEE Trans. Inf. Forensics Secur. 12(12), 2890–2905 (2017)

    Article  Google Scholar 

  23. Piva, A.: An overview on image forensics. International Scholarly Research Notices 2013 (2013)

    Google Scholar 

  24. Shashidhar, T., Ramesh, K.: Reviewing the effectivity factor in existing techniques of image forensics. Int. J. Electr. Comput. Eng. (IJECE) 7(6), 3558–3569 (2017)

    Article  Google Scholar 

  25. Swaminathan, A., Wu, M., Liu, K.R.: Digital image forensics via intrinsic fingerprints. IEEE Trans. Inf. Forensics Secur. 3(1), 101–117 (2008)

    Article  Google Scholar 

  26. Lahousen, T., Linder, D., Gieler, T., Gieler, U.: Der Hautarzt 68(12), 973–979 (2017). https://doi.org/10.1007/s00105-017-4064-7

  27. Verdoliva, L.: Media forensics and DeepFakes: an overview. IEEE J. Sel. Top. Signal Process. 14(5), 910–932 (2020)

    Article  Google Scholar 

  28. Van der Walt, S., et al.: scikit-image: image processing in Python. PeerJ 2, e453 (2014)

    Article  Google Scholar 

  29. Yang, P., Baracchi, D., Ni, R., Zhao, Y., Argenti, F., Piva, A.: A survey of deep learning-based source image forensics. J. Imaging 6(3), 9 (2020)

    Article  Google Scholar 

  30. Yerushalmy, I., Hel-Or, H.: Digital image forgery detection based on lens and sensor aberration. Int. J. Comput. Vision 92(1), 71–91 (2011)

    Article  Google Scholar 

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Acknowledgements

The second authors is supported by the Austrian Science Fund (FWF) under grant no. I 4057-N31 (“Game Over Eva(sion)”).

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Authors and Affiliations

  1. Digital Business and Software Engineering, Management Center Innsbruck, Innsbruck, Austria

    Daniel Aumayr & Pascal Schöttle

Authors
  1. Daniel Aumayr
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  2. Pascal Schöttle
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Corresponding author

Correspondence to Daniel Aumayr .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Stan Sclaroff

  2. National Research Council, Lecce, Italy

    Cosimo Distante

  3. National Research Council, Lecce, Italy

    Marco Leo

  4. University of Catania, Catania, Italy

    Giovanni M. Farinella

  5. Technische Universität München, Garching, Germany

    Federico Tombari

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Aumayr, D., Schöttle, P. (2022). U Can’t (re)Touch This – A Deep Learning Approach for Detecting Image Retouching. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_11

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  • DOI: https://doi.org/10.1007/978-3-031-06430-2_11

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  • Online ISBN: 978-3-031-06430-2

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