Skip to main content

Advertisement

Log in

Double salted HMAC signature with blockchain for faster and secure video integrity verification

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Digital technology advancements have increased the use of video surveillance for ongoing observation, aiding in forensic investigations and crime prevention. Recent developments in video editing and manipulation software, however, make it simple to alter footage without leaving obvious traces. Therefore, before being used as evidence, video data need to have its integrity verified. In this paper, a novel and lightweight method for ensuring the integrity of video data is presented. Blockchain, Hash-based message authentication code using BLAKE2b hash function, and Twisted Edwards Curve to generate signatures and Diffie-Hellman algorithm using Curve25519 for exchange key are used in the proposed method. The file location for the video segment, the double salted HMAC signature, and the transient public key needed to validate the signature are all included in each block in the chain. The double salted HMAC signature is the combined signature of salted HMAC value of the video segment and salted HMAC value of previous block. Recomputing the salted HMAC values allows for the validation of this signature at the time of verification. According to experimental data, the proposed method is faster and more secure than state-of-the-art methods. With negligible additional storage requirements, our method can detect every kind of forgery on any video file, by an authorized user. Additionally, our security analysis demonstrates that our method is resistant to side-channel, differential, preimage, and key substitution attacks, among other forms of assaults. The proposed lightweight video integrity verification method is more appropriate for usage in devices with limited resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Algorithm 1
Algorithm 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

https://github.com/linjuajith/HMACsign.

References

  1. 029-realistic beautiful flower painting timelapse by artistbrownlion– satisfying video–2.5 min (2022) https://www.youtube.com/watch?v=2g8bS-_nNYE&t=7s/. Accessed on January

  2. 1 min of nature footage–4k (ultra hd) (2022) https://www.youtube.com/watch?v=WLKJnHu0GC4/. Accessed on January

  3. 4k video ultra hd–epic footage (2022) https://www.youtube.com/watch?v=od5nla42Jvc/. Accessed on January

  4. Avs video editor (2022) https://www.avs4you.com/avs-video-editor.aspx/. Accessed on January

  5. Derf’s collection (2022) https://media.xiph.org/video/derf/. Accessed on January

  6. Ffmpeg (2022) http://www.ffmpeg.org/. Accessed on January

  7. Nature in 30 seconds (2022) https://www.youtube.com/watch?v=MHna8CzxPLk/. Accessed on January

  8. Real yellow car (2022) https://www.youtube.com/watch?v=o2YNaYcwdbA/. Accessed on January

  9. Video quality experts group (2024) https://www.its.bldrdoc.gov/vqeg/vqeg-home.aspx Accessed on January

  10. Amerini I, Galteri L, Caldelli R, Del Bimbo A (2019) Deepfake video detection through optical flow based cnn. In: Proceedings of the IEEE/CVF international conference on computer vision workshops, pp 0–0

  11. Aumasson JP, Neves S, Wilcox-O’Hearn Z, Winnerlein C (2013) Blake2: simpler, smaller, fast as md5. In: Applied Cryptography and Network Security: 11th International Conference, ACNS 2013, Banff, AB, Canada, June 25–28, 2013. Proceedings 11. Springer, pp 119–135

  12. Bakas J, Naskar R, Bakshi S (2021) Detection and localization of inter-frame forgeries in videos based on macroblock variation and motion vector analysis. Comput Electr Eng 89:106929

    Article  MATH  Google Scholar 

  13. Barnard EM (2015) Tutorial of twisted edwards curves in elliptic curve cryptography. UC SANTA BARBARA, CS 290

  14. Bernstein DJ (2006) Curve25519: new diffie-hellman speed records. In: Public Key Cryptography-PKC 2006: 9th International Conference on Theory and Practice in Public-Key Cryptography, New York, April 24–26, 2006. Proceedings 9. Springer, pp 207–228

  15. Bohli JM, Röhrich S, Steinwandt R (2006) Key substitution attacks revisited: taking into account malicious signers. Int J Inf Secur 5(1):30–36

    Article  MATH  Google Scholar 

  16. Brendel J, Cremers C, Jackson D, Zhao M (2021) The provable security of ed25519: theory and practice. In: 2021 IEEE Symposium on Security and Privacy (SP). IEEE, pp 1659–1676

  17. Casey E, Souvignet TR (2020) Digital transformation risk management in forensic science laboratories. Forensic Sci Int 316:110486

    Article  MATH  Google Scholar 

  18. Dobre RA, Preda RO, Oprea CC, Pirnog I (2018) Authentication of jpeg images on the blockchain. In: 2018 International Conference on Control, Artificial Intelligence, Robotics and Optimization (ICCAIRO). IEEE, pp 211–215.

  19. Dong J, Zheng F, Cheng J, Lin J, Pan W, Wang Z (2018) Towards high-performance x25519/448 key agreement in general purpose gpus. In: 2018 IEEE Conference on Communications and Network Security (CNS). IEEE, pp 1–9

  20. El Rai MC, Al Ahmad H, Gouda O, Jamal D, Talib MA, Nasir Q (2020) Fighting deepfake by residual noise using convolutional neural networks. In: 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS). IEEE, pp 1–4

  21. Galbraith SD (2012) Mathematics of public key cryptography. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  22. Geradts Z, Riphagen Q (2023) Interpol review of forensic video analysis, 2019–2022. Forensic Sci Int: Synerg. 6:100309

    Google Scholar 

  23. Ghimire S, Choi JY, Lee B (2019) Using blockchain for improved video integrity verification. IEEE Trans Multimedia 22(1):108–121

    Article  MATH  Google Scholar 

  24. Ghimire S, Lee B (2020) A data integrity verification method for surveillance video system. Multimedia Tools Appl 79:30163–30185

    Article  MATH  Google Scholar 

  25. Guo Z, Yang G, Chen J, Sun X (2021) Fake face detection via adaptive manipulation traces extraction network. Comput Vis Image Underst 204:103170

    Article  Google Scholar 

  26. Hays J, Efros AA (2007) Scene completion using millions of photographs. ACM Trans Graph (ToG) 26(3):4

    Article  MATH  Google Scholar 

  27. He P, Jiang X, Sun T, Wang S (2017) Detection of double compression in mpeg-4 videos based on block artifact measurement. Neurocomputing 228:84–96

    Article  MATH  Google Scholar 

  28. He P, Jiang X, Sun T, Wang S, Li B, Dong Y (2017) Frame-wise detection of relocated i-frames in double compressed h. 264 videos based on convolutional neural network. J Vis Commun Image Represent 48:149–158

    Article  Google Scholar 

  29. Hong JH, Yang Y, Oh BT (2019) Detection of frame deletion in hevc-coded video in the compressed domain. Digit Investig 30:23–31

    Article  MATH  Google Scholar 

  30. Huamán CQ, Orozco ALS, Villalba LJG (2020) Authentication and integrity of smartphone videos through multimedia container structure analysis. Futur Gener Comput Syst 108:15–33

    Article  Google Scholar 

  31. Iuliani M, Shullani D, Fontani M, Meucci S, Piva A (2018) A video forensic framework for the unsupervised analysis of mp4-like file container. IEEE Trans Inf Forensics Secur 14(3):635–645

    Article  Google Scholar 

  32. Jin X, Su Y, Jing P (2022) Video frame deletion detection based on time-frequency analysis. J Vis Commun Image Represent 83:103436

    Article  MATH  Google Scholar 

  33. Kanwal N, Asghar MN, Ansari MS, Fleury M, Lee B, Herbst M, Qiao Y (2020) Preserving chain-of-evidence in surveillance videos for authentication and trust-enabled sharing. IEEE Access 8:153413–153424

    Article  Google Scholar 

  34. Kim M, Kim KY (2014) Data forgery detection for vehicle black box. In: 2014 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, pp 636–637

  35. Kwatra V, Schödl A, Essa I, Turk G, Bobick A (2003) Graphcut textures: image and video synthesis using graph cuts. Acm Trans Graph (tog) 22(3):277–286

    Article  Google Scholar 

  36. Kwon H, Kim S, Lee H (2016) Sigmata: storage integrity guaranteeing mechanism against tampering attempts for video event data recorders. International Institute of Informatics and Systemics, IIIS

  37. Lawrence L, Shreelekshmi R (2021) Chained digital signature for the improved video integrity verification. In: International Conference on Machine Learning and Intelligent Systems(MLIS), pp 520–526

  38. Lawrence L, Shreelekshmi R (2023) Video integrity checking using x25519 and nested hmac with blake2b. In: International Conference on Advances in Data-driven Computing and Intelligent Systems. Springer, pp 411–422

  39. Lawrence L, Shreelekshmi R (2024) Edwards curve digital signature algorithm for video integrity verification on blockchain framework. Sci Justice. https://doi.org/10.1016/j.scijus.2024.04.008

    Article  MATH  Google Scholar 

  40. Marton K, Suciu A (2015) On the interpretation of results from the NIST statistical test suite. Sci Technol 18(1):18–32

    MATH  Google Scholar 

  41. Oh S, Hoogs A, Perera A, Cuntoor N, Chen CC, Lee JT, Mukherjee S, Aggarwal J, Lee H, Davis L et al (2011) A large-scale benchmark dataset for event recognition in surveillance video. In: CVPR 2011. IEEE, pp 3153–3160

  42. Orozco ALS, Huamán CQ, Álvarez DP, Villalba LJG (2020) A machine learning forensics technique to detect post-processing in digital videos. Futur Gener Comput Syst 111:199–212

    Article  Google Scholar 

  43. Patwardhan KA, Sapiro G, Bertalmío M (2007) Video inpainting under constrained camera motion. IEEE Trans Image Process 16(2):545–553

    Article  MathSciNet  MATH  Google Scholar 

  44. Pereida García C, Sovio S (2021) Size, speed, and security: An ed25519 case study. In: Secure IT Systems: 26th Nordic Conference, NordSec 2021, Virtual Event, November 29–30, 2021, Proceedings 26. Springer, pp 16–30

  45. Qadir G, Yahaya S, Ho AT (2012) Surrey university library for forensic analysis (SULFA) of video content

  46. Saarinen MJ, Aumasson JP (2015) The blake2 cryptographic hash and message authentication code (mac). Tech Rep

  47. Seemanthini K Manjunath SS, RAS (2019) Detection of video and multimedia copy-move forgery using optical algorithm and glsm clustering. International Journal of Innovative Technology and Exploring Engineering, vol. 9. pp 200–205

  48. Singh RD AN (2017) Detection of upscale-crop and splicing for digital video authentication. Digit Investig 21:31–52

    Article  MATH  Google Scholar 

  49. Vazquez-Padin D, Fontani M, Bianchi T, Comesaña P, Piva A, Barni M (2012) Detection of video double encoding with gop size estimation. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, pp 151–156

  50. Vázquez-Padín D, Fontani M, Shullani D, Pérez-González F, Piva A, Barni M (2019) Video integrity verification and GOP size estimation via generalized variation of prediction footprint. IEEE Trans Inf Forensics Secur 15:1815–1830

    Article  Google Scholar 

  51. Yu L, Wang H, Han Q, Niu X, Yiu SM, Fang J, Wang Z (2016) Exposing frame deletion by detecting abrupt changes in video streams. Neurocomputing 205:84–91

    Article  Google Scholar 

  52. Zhang Z, Mal C, Ding B, Gao M (2021) Detecting manipulated facial videos: a time series solution. In: 2020 25th international conference on pattern recognition (ICPR). IEEE, pp 2817–2823

  53. Zhong JL, Pun CM, Gan YF (2020) Dense moment feature index and best match algorithms for video copy-move forgery detection. Inf Sci 537:184–202

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

Authors extend gratitude to University Grant Commission, Government of India, for granting the research fellowship.

Author information

Authors and Affiliations

Authors

Contributions

Linju Lawrence was contributed conceptualization, investigation, methodology, software, validation, formal analysis, data curation, visualization, writing—original draft, and writing—review and editing. Shreelekshmi R was involved in formal analysis, supervision, visualization, and writing—review and editing.

Corresponding author

Correspondence to Linju Lawrence.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lawrence, L., Shreelekshmi, R. Double salted HMAC signature with blockchain for faster and secure video integrity verification. J Supercomput 81, 598 (2025). https://doi.org/10.1007/s11227-025-06996-3

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-025-06996-3

Keywords