Source camera attribution using a rule-based explainable convolutional neural network – Scientific Reports

1. Nayerifard, T., Amintoosi, H., Gaemi Bafghi, A. and Dehghantanha, A. Machine learning in digital forensics: A systematic literature review. https://doi.org/10.48550/arXiv.2306.04965 (2023).

2. Hall, S. W., Sakzad, A. & Choo, K. R. Explainable artificial intelligence for digital forensics. WIREs Forensic Sci. 4, 1–11. https://doi.org/10.1002/wfs2.1434 (2022).

   Google Scholar 

3. Fähndrich, J. et al. Digital forensics and strong AI: A structured literature review. Forensic Sci. Int. Digit. Investig. https://doi.org/10.1016/j.fsidi.2023.301617 (2023).

   Google Scholar 

4. Solanke, A. A. Explainable digital forensics AI: Towards mitigating distrust in AI-based digital forensics analysis using interpretable models. Forensic Sci. Int. Digit. Investig. https://doi.org/10.1016/j.fsidi.2022.301403 (2022).

   Google Scholar 

5. Ali, S. et al. Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Inf. Fusion https://doi.org/10.1016/j.inffus.2023.101805 (2023).

   Google Scholar 

6. IEEE Standards Association, Guide for an Architectural Framework for Explainable Artificial Intelligence. IEEE standard 2894 https://doi.org/10.1109/IEEESTD.2024.10659410 (2024).

7. Hall, S. W., Sakzad, A. & Minagar, S. A proof of concept implementation of explainable artificial intelligence (XAI) in digital forensics. In Network and System Security Conference https://doi.org/10.1007/978-3-031-23020-2_4 (2022).

8. Lo, W., Kulatilleke, G., Sarhan, M., Layeghy, S. & Portmann, M. XG-BoT: An explainable deep graph neural network for botnet detection and forensics. Internet Things https://doi.org/10.1016/j.iot.2023.100747 (2023).

   Google Scholar 

9. Henrique, S. et al. Forensic Science International: Synergy Deepfake forensics analysis: An explainable hierarchical ensemble of weakly supervised models. IForensic Sci. Int. Synerg. https://doi.org/10.1016/j.fsisyn.2022.100217 (2022).

   Google Scholar 

10. Ge, W. et al. MetaCluster: A universal interpretable classification framework for cybersecurity. IEEE Trans. Inf. Forensics Security 19, 3829–3843. https://doi.org/10.1109/TIFS.2024.3372808 (2024).

    Google Scholar 

11. Floreale, G. et al. Automated processing of eXplainable Artificial Intelligence outputs in deep learning models for fault diagnostics of large infrastructures. Eng. Appl. Artif. Intell. 149, 110518 (2025).

    Google Scholar 

12. Lundberg, S. M. and Lee, S. I. A unified approach to interpreting model predictions. IAdv. Neural Inf. Process. Syst. Preprint at https://doi.org/10.48550/arXiv.1705.07874 (2017).

13. Nayerifard, T., Amintoosi, H. & Gaemi Bafghi, A. A robust PRNU-based source camera attribution with convolutional neural networks. J. Supercomput. https://doi.org/10.1007/s11227-024-06579-8 (2025).

    Google Scholar 

14. Shullani, D., Fontani, M., Iuliani, M., Shaya, OAl. and Piv, A. VISION: A video and image dataset for source identification. EURASIP J. Inf. Secur. https://doi.org/10.1186/s13635-017-0067-2 (2017)

15. Selvaraju, R. R. et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. IInt. J. Comput. Vis. 128, 336–359. https://doi.org/10.1109/ICCV.2017.74 (2020).

    Google Scholar 

16. Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. In 34th International Conference on Machine Learning, ICML 7, 5109–5118 https://doi.org/10.48550/arXiv.1703.01365 (2017).

17. Ghai, B., Vera Liao, Q., Zhang, Y., Bellamy, R. and Mueller, K. Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers Proc. ACM Human-Computer Interaction 4, 1–28, https://doi.org/10.1145/3432934 (2020).

18. Rasmussen, C. B., Kirk, K. & Moeslund, T. B. The challenge of data annotation in deep learning—A case study on whole plant corn silage. Sensors https://doi.org/10.3390/s22041596 (2022).

    Google Scholar 

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Authors and Affiliations

1. Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

   Tahereh Nayerifard, Haleh Amintoosi & Abbas Ghaemi Bafghi

Contributions

T.N. implemented the proposed idea and extracted the results. T. N also prepared the first draft. H.A. revised the paper and supervised the whole process. H.A. and A.GhB. helped with the formation of the basic idea.

Corresponding author

Correspondence to Haleh Amintoosi.

Competing interests

The authors declare no competing interests.

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