Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence

Bilgehan Arslan

doi:10.55859/ijiss.1826477

Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence

Abstract

This study introduces Causal Image Processing (CIP), a mechanism based conceptual framework designed to address fundamental weaknesses of contemporary deep learning systems in security critical visual intelligence. Unlike conventional convolutional networks, vision transformers, or diffusion models that rely on observational correlations, CIP models the image formation process through four independent causal mechanisms: content, domain conditions, sensor characteristics, and identity factors. These mechanisms are formalized within a directed acyclic graph that provides a structured representation separating the physical and semantic processes underlying image generation. CIP integrates three forms of inference within a unified architecture: predictive inference, interventional inference, and counterfactual inference. Mechanism faithful representations enforce invariance to sensor and domain variations, while counterfactual reasoning enables principled evaluation of identity consistency under hypothetical acquisition conditions. The framework also introduces learning principles based on mechanism fidelity, sparse intervention sensitivity, and causal invariance, and defines evaluation criteria centered on counterfactual consistency and tamper resistance. Overall, CIP offers a theoretical foundation for developing robust, explainable, and tamper resistant vision systems capable of operating reliably across heterogeneous sensors, environments, and acquisition conditions. The framework establishes a mechanism centered alternative to correlation driven deep learning pipelines and provides a pathway for next generation secure visual intelligence.

Keywords

References

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Details

Primary Language

English

Subjects

Digital Forensics, Data Security and Protection

Journal Section

Research Article

Authors

Bilgehan Arslan ^*
0000-0002-5160-4408
Türkiye

Publication Date

March 19, 2026

Submission Date

November 19, 2025

Acceptance Date

January 9, 2026

Published in Issue

Year 2026 Volume: 15 Number: 1

DOI

https://doi.org/10.55859/ijiss.1826477

IZ

https://izlik.org/JA76LH58XT

Cite

RIS / Bibtex

APA

Arslan, B. (2026). Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence. International Journal of Information Security Science, 15(1), 1-24. https://doi.org/10.55859/ijiss.1826477

AMA

1.Arslan B. Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence. IJISS. 2026;15(1):1-24. doi:10.55859/ijiss.1826477

Chicago

Arslan, Bilgehan. 2026. “Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence”. International Journal of Information Security Science 15 (1): 1-24. https://doi.org/10.55859/ijiss.1826477.

EndNote

Arslan B (March 1, 2026) Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence. International Journal of Information Security Science 15 1 1–24.

IEEE

[1]B. Arslan, “Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence”, IJISS, vol. 15, no. 1, pp. 1–24, Mar. 2026, doi: 10.55859/ijiss.1826477.

ISNAD

Arslan, Bilgehan. “Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence”. International Journal of Information Security Science 15/1 (March 1, 2026): 1-24. https://doi.org/10.55859/ijiss.1826477.

JAMA

1.Arslan B. Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence. IJISS. 2026;15:1–24.

MLA

Arslan, Bilgehan. “Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence”. International Journal of Information Security Science, vol. 15, no. 1, Mar. 2026, pp. 1-24, doi:10.55859/ijiss.1826477.

Vancouver

1.Bilgehan Arslan. Causal Image Processing: A Mechanism-Based Framework for Secure Visual Intelligence. IJISS. 2026 Mar. 1;15(1):1-24. doi:10.55859/ijiss.1826477