UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection

Accepted to CVPR 2026.

Image generation and generated-image detection have both advanced rapidly, but mostly along separate technical paths: generation is dominated by generative architectures, while detection is dominated by discriminative ones. This separation creates a persistent gap in practice: generators are not directly optimized by forensic criteria, and detectors are often trained on static snapshots of old forgeries, which limits robustness to new generators.

UniGenDet addresses this gap with a unified co-evolutionary framework that jointly optimizes generation and detection in one loop. The core idea is to make both tasks explicitly exchange useful signals instead of evolving independently.

• Symbiotic multimodal self-attention bridges generation and authenticity understanding in a shared architecture.
• Generation-detection unified fine-tuning (GDUF) equips the detector with generative priors, improving generalization and interpretability.
• Detector-informed generative alignment (DIGA) feeds authenticity constraints back into synthesis, improving realism and fidelity.

In short, UniGenDet turns the traditional "generator vs. detector" arms race into a closed-loop collaboration. This repository provides the full training and evaluation pipeline built on pretrained BAGEL components.