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

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arslan b.

INTERNATIONAL JOURNAL OF INFORMATION SECURITY SCIENCE, cilt.15, sa.1, ss.1-24, 2026 (TRDizin)

Özet

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.