Unified Task Conditioning
The same conditional control module processes forgery evidence in both tasks. Constrained localization additionally compares the manipulated image with its source image through shared feature extraction.
Image manipulation localization must handle both open-world forgeries and constrained settings where an original reference image is available. UGD-IML unifies these tasks with a generative diffusion framework that iteratively denoises a manipulation mask. A conditional control module extracts image guidance, while task-specific conditioning and noise embeddings allow one model to support both settings. Multi-step generation also exposes uncertainty around difficult boundaries and improves robustness across manipulation types.
The same conditional control module processes forgery evidence in both tasks. Constrained localization additionally compares the manipulated image with its source image through shared feature extraction.
UGD-IML localizes diverse edited regions with coherent masks in both unconstrained and source-guided scenarios.
Variation across diffusion samples provides an uncertainty estimate that concentrates near ambiguous manipulation boundaries and often aligns with localization errors.
@article{mi2026ugdiml,
title={{UGD-IML}: A Unified Generative Diffusion-based Framework for Constrained and Unconstrained Image Manipulation Localization},
author={Mi, Yachun and He, Xingyang and Sun, Shixin and Li, Yu and Li, Zhixuan and Jin, Jian and Hui, Chen and Liu, Shaohui},
journal={Neurocomputing},
year={2026},
publisher={Elsevier}
}