SynerGenDet: Internal Flow Forensics and Timestep Adaptive Alignment for Unified Image Generation and Detection

Image generation and generated-image detection have both advanced rapidly, but they iterate independently for a long time, lacking a mechanism for mutual promotion and co-evolution. Although some recent work has attempted to use a unified architecture to achieve co-evolution of generation and detection, it has not fully leveraged the advantages of a unified architecture: detection remains a purely semantic judgment, while generation does not effectively exploit detection feedback. Therefore, we propose SynerGenDet: a unified framework that deeply couples generation and detection. On the detection side, we leverage the model’s native generative capability for authenticity discrimination in a single unified forward pass, without requiring an auxiliary generative model or iterative denoising. Specifically, we use its one-step flow matching error as a generative forensic cue that complements semantic evidence. On the generation side, we introduce Timestep adaptive Alignment, which guides image generation with detector feedback at the right coarse-to-fine granularity. Experiments on multiple benchmarks show that SynerGenDet achieves state-of-the-art performance.