LocateEdit-Bench: A Benchmark for Instruction-Based Editing Localization
This addresses the challenge of keeping forgery localization methods up-to-date with evolving image editing techniques, though it is incremental as it focuses on dataset creation rather than a new localization method.
The authors tackled the problem of localizing AI-generated image forgeries by introducing LocateEdit-Bench, a large-scale dataset with 231K edited images, which benchmarks localization methods against instruction-based editing and shows existing methods are ineffective for this new paradigm.
Recent advancements in image editing have enabled highly controllable and semantically-aware alteration of visual content, posing unprecedented challenges to manipulation localization. However, existing AI-generated forgery localization methods primarily focus on inpainting-based manipulations, making them ineffective against the latest instruction-based editing paradigms. To bridge this critical gap, we propose LocateEdit-Bench, a large-scale dataset comprising $231$K edited images, designed specifically to benchmark localization methods against instruction-driven image editing. Our dataset incorporates four cutting-edge editing models and covers three common edit types. We conduct a detailed analysis of the dataset and develop two multi-metric evaluation protocols to assess existing localization methods. Our work establishes a foundation to keep pace with the evolving landscape of image editing, thereby facilitating the development of effective methods for future forgery localization. Dataset will be open-sourced upon acceptance.