The Art of Retrieval Meets Learning: RL-Guided Best-of-N and Auxiliary-Head Fusion for Style-Transfer Detection

The rapid progress of generative imaging has made authenticity detection increasingly difficult, particularly when synthetic content also imitates established artistic styles. We introduce a reinforcement-guided Best-of-$N$ retrieval framework that recasts multimodal retrieval as sequential evidence acquisition. Rather than aggregating a fixed set of nearest neighbors, a lightweight policy ${\pi }_{\theta }(a_t\mid s_t)$ learns when to retrieve additional evidence, switch modality, expand spatial context, reject unreliable candidates, abstain, or stop. Local visual representations are combined with LLM-generated surface descriptors, while the visual and textual encoders remain frozen at inference time. This formulation enables adaptive filtering of noisy or redundant retrieval evidence and improves style-aware representation separation. On AI-ArtBench, the proposed retrieval policy achieves 95.41% mean task accuracy across authenticity and style prediction, while also showing strong performance on additional real/synthetic and deepfake benchmarks. We further introduce an alternative trainable Auxiliary-Head Model that integrates global style, local artifacts, and cross-region coherence through a soft expert-fusion consistency regularizer, achieving 93.96% accuracy. Together, the two approaches provide distinct operating points for style-aware authenticity detection: adaptive retrieval with frozen encoders and trainable single-pass prediction.