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Using Perceptual Hashing for Targeted Content Scanning

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Advances in Digital Forensics XIX (DigitalForensics 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 687))

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Abstract

The Internet is increasingly used to disseminate unethical and illegal content. A grave concern is child sexual abuse material that is often disseminated via end-to-end-encrypted channels. Such encryption defeats network- and server-based scanning measures used by law enforcement. A trade-off is to enable confidential communications channels for users and scanning opportunities for law enforcement by employing perceptual-hashing-based targeted content scanning on user devices. This has generated intense discussions between policymakers, privacy advocates and child protection organizations.

This chapter summarizes the current state of reserch in perceptual-hashing-based targeted content scanning with a focus on classical metrics such as false positives, false negatives and privacy aspects. Insights are provided into the most relevant perceptual hashing methods and an attack taxonomy for perceptual-hashing-based targeted content scanning is presented. The complexity in generating false negatives is evaluated and the feasibility of evading perceptual-hashing-based targeted content scanning is demonstrated.

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References

  1. H. Abelson, R. Anderson, S. Bellovin, J. Benaloh, M. Blaze, J. Callas, W. Diffie, S. Landau, P. Neumann, R. Rivest, J. Schiller, B. Schneier, V. Teague and C. Troncoso, Bugs in our pockets: The risks of client-side scanning, arXiv: 2110.07450v1 (arxiv.org/abs/2110.07450v1), 2021.

  2. Apple, CSAM Detection – Technical Summary, Cupertino, California (www.apple.com/child-safety/pdf/CSAM_Detection_Technical_Summary.pdf), 2021.

  3. Apple, Security Threat Model Review of Apple’s Child Safety Features, Cupertino, California (www.apple.com/child-safety/pdf/Security_Threat_Model_Review_of_Apple_Child_Safety_Features.pdf), 2021.

  4. A. Athalye, Inverting PhotoDNA, Internet Archive, December 20, 2021.

    Google Scholar 

  5. A. Athalye, NeuralHash Collider, GitHub (github.com/anishathalye/neural-hash-collider), 2023.

    Google Scholar 

  6. A. Bhowmick, D. Boneh, S. Myers, K. Talwar and K. Tarbe, The Apple PSI System, Apple, Cupertino, California (www.apple.com/child-safety/pdf/Apple_PSI_System_Security_Protocol_and_Analysis.pdf), 2021.

  7. F. Breitinger, B. Guttman, M. McCarrin, V. Roussev and D. White, Approximate Matching: Definition and Terminology, NIST Special Publication 800-168, National Institute of Standards and Technology, Gaithersburg, Maryland, 2014.

    Google Scholar 

  8. Facebook, Online child protection – Tools and technology, Internet Archive, June 21, 2022.

    Google Scholar 

  9. Federal Criminal Police Office, Presentation of the Numbers of Child Victims of Violence – Evaluation of the Police Crime Statistics 2021 (in German), Wiesbaden, Germany (www.bka.de/SharededDocs/Downloads/DE/AktuelleInformationen/Infografiken/Sonstige/kindlicheGewaltopfer_PKS2021.pdf), 2022.

  10. Q. Hao, L. Luo, S. Jan and G. Wang, It’s not what it looks like: Manipulating perceptual-hashing-based applications, Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, pp. 69–85, 2021.

    Google Scholar 

  11. S. Jain, A. Cretu and Y. Montjoye, Adversarial detection avoidance attacks: Evaluating the robustness of perceptual-hashing-based client-side scanning, Proceedings of the Thirty-First USENIX Security Symposium, pp. 2317–2334, 2022.

    Google Scholar 

  12. D. Kingma and J. Ba, Adam: A method for stochastic optimization, poster paper presented at the Third International Conference on Learning Representations, 2015.

    Google Scholar 

  13. E. Klinger and D. Starkweather, pHash: The open source perceptual hash library, Internet Archive, April 9, 2023.

    Google Scholar 

  14. N. Krawetz, Looks like it, Internet Archive, May 26, 2011.

    Google Scholar 

  15. N. Krawetz, Kind of like that, Internet Archive, January 21, 2013.

    Google Scholar 

  16. A. Kulshrestha and J. Mayer, Identifying harmful media in (end-to-end) encrypted communications: Efficient private membership computation, Proceedings of the Thirtieth USENIX Security Symposium, pp. 893–910, 2021.

    Google Scholar 

  17. Meta, Open-sourcing photo- and video-matching technology to make the Internet safer, Internet Archive, August 1, 2019.

    Google Scholar 

  18. Meta, The TMK+PDQF Video-Hashing Algorithm and the PDQ Image Hashing Algorithm, Menlo Park, California (raw.githubusercontent.com/facebook/ThreatExchange/main/hashing/hashing.pdf), 2021.

    Google Scholar 

  19. Microsoft, Photo DNA, Internet Archive, April 11, 2023.

    Google Scholar 

  20. National Center for Missing and Exploited Children, CyberTipline 2021 Report, Internet Archive, March 17, 2022.

    Google Scholar 

  21. D. Petrov, Wavelet image hash in Python, Internet Archive, July 2, 2016.

    Google Scholar 

  22. J. Prokos, T. Jois, N. Fendley, R. Schuster, M. Green, E. Tromer and Y. Cao, Squint hard enough: Evaluating perceptual hashing with machine learning, Cryptology ePrint Archive, paper no. 2021/1531 (eprint.iacr.org/2021/1531), 2021.

    Google Scholar 

  23. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. Berg and F. Li, ImageNet large scale visual recognition challenge, International Journal of Computer Vision, vol. 115(3), pp. 211–252, 2015.

    Google Scholar 

  24. L. Struppek, Learning-to-Break-Deep-Perceptual-Hashing, GitHub (github.com/ml-research/Learning-to-Break-Deep-Perceptual-Hashing), 2022.

    Google Scholar 

  25. L. Struppek, D. Hintersdorf, D. Neider and K. Kersting, Learning to break deep perceptual hashing: The use case NeuralHash, Proceedings of the ACM Conference on Fairness, Accountability and Transparency, pp. 58–69, 2022.

    Google Scholar 

  26. K. Walker, Four steps we’re taking today to fight terrorism online, Internet Archive, June 18, 2017.

    Google Scholar 

  27. Z. Wang, A. Bovik, H. Sheikh and E. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing, vol. 13(4), pp. 600–612, 2004.

    Google Scholar 

  28. WeProtect Global Alliance, Global Threat Assessment 2021, Sevenoaks, United Kingdom (www.weprotect.org/wp-content/uploads/Global-Threat-Assessment-2021.pdf), 2021.

  29. B. Yang, F. Gu and X. Niu, Block mean value based image perceptual hashing, Proceedings of the International Conference on Intelligent Information Hiding and Multimedia, pp. 167–172, 2006.

    Google Scholar 

  30. C. Zauner, Implementation and Benchmarking of Perceptual Image Hash Functions, M.S. Thesis, Secure Information Systems Program, University of Applied Sciences Upper Austria, Hagenberg, Austria, 2010.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Bundeswehr University, Munich, Germany

    Leon Twenning, Harald Baier & Thomas Göbel

Authors
  1. Leon Twenning
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  2. Harald Baier
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  3. Thomas Göbel
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Corresponding author

Correspondence to Leon Twenning .

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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH, USA

    Gilbert Peterson

  2. Keplinger Hall 3315, University of Tulsa, Tulsa, OK, USA

    Sujeet Shenoi

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Twenning, L., Baier, H., Göbel, T. (2023). Using Perceptual Hashing for Targeted Content Scanning. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics XIX. DigitalForensics 2023. IFIP Advances in Information and Communication Technology, vol 687. Springer, Cham. https://doi.org/10.1007/978-3-031-42991-0_7

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  • DOI: https://doi.org/10.1007/978-3-031-42991-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42990-3

  • Online ISBN: 978-3-031-42991-0

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