Trustworthiness and Authenticity in the Age of AI: Evidence-Oriented and Reliability-Aware Framework for Image Forgery Detection

The increasing accessibility of advanced image editing and generative models has strengthened the need for reliable passive image forgery detection and localization. This paper reviews representative methods for identifying manipulated images and regions without access to the original image or embedded authentication information. We introduce an evidence- and reasoning-oriented taxonomy that organizes existing approaches into five categories: acquisition consistency and noise, compression and spectral consistency, spatial correspondence and structural reasoning, multi-evidence and contextual reasoning, and generative-editing and foundation-model reasoning. The review further examines commonly used datasets, evaluation practices, and robustness settings, showing how annotation quality, manipulation realism, post-processing, and generator diversity influence reported performance. Drawing on the limitations identified across the literature, we outline the Multi-Evidence Reliability-Aware Forgery Analysis framework as a conceptual reference framework for condition-aware evidence extraction, reliabilityaware fusion, uncertainty estimation, and evidence-grounded reporting. The analysis indicates that future progress depends not only on stronger architectures, but also on transferable forensic representations, realistic evaluation, calibrated uncertainty, and reliable operation under unseen manipulations and distribution conditions.