Forensic Prompting with Dual-Action Policy Optimization for Vision-Language Forgery Detection and Localization

Image forgery is rapidly evolving, rendering forensic traces increasingly subtle and readily attenuated by post-processing. Although vision-language prompting can inject priors, open-ended LLM-generated prompts are difficult to constrain, and naive language descriptions can introduce semantic perturbations. To address these challenges, we propose Forensic Prompting with Dual-Action policy optimization (FPDA) for vision-language forgery detection and localization, where the Forensic Prompting Module (FPM) constructs a structured and reproducible forensic prompt bank and supports optional text input as a reliability-aware cue for stable conditioning. Moreover, Dual-Action Policy Optimization (DAPO) is applied to learn sample-adaptive evidence usage by routing forensic prompts and scheduling localization refinement on a per-image basis, stabilizing discriminative cues and improving mask spatial consistency. Extensive experiments are conducted on multiple public datasets covering manual manipulations, diffusion content, face forgeries, and text-enabled settings, demonstrating favorable detection and localization performance over representative state-of-the-art methods under comparable evaluation protocols.