In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fields have evolved distinct architectural paradigms: the former predominantly relies on generative networks, while the latter favors discriminative frameworks. A recent trend in both domains is the use of adversarial information to enhance performance, revealing potential for synergy. However, the significant architectural divergence between them presents considerable challenges. Departing from previous approaches, we propose UniGenDet: a Unified generative-discriminative framework for co-evolutionary image Generation and generated image Detection. To bridge the task gap, we design a symbiotic multimodal self-attention mechanism and a unified fine-tuning algorithm. This synergy allows the generation task to improve the interpretability of authenticity identification, while authenticity criteria guide the creation of higher-fidelity images. Furthermore, we introduce a detector-informed generative alignment mechanism to facilitate seamless information exchange. Extensive experiments on multiple datasets demonstrate that our method achieves state-of-the-art performance. Code: \href{https://github.com/Zhangyr2022/UniGenDet}{https://github.com/Zhangyr2022/UniGenDet}.

翻译：近年来，图像生成与生成图像检测领域均取得了显著进展。尽管这两个领域发展迅速且相对独立，但它们已演化出不同的架构范式：前者主要依赖生成网络，后者则偏好判别框架。两个领域中最近的一个趋势是使用对抗信息来提升性能，这揭示了二者间的协同潜力。然而，显著的架构差异带来了巨大挑战。与以往方法不同，我们提出UniGenDet：一个用于图像生成与生成图像检测协同演进的统一生成-判别框架。为弥合任务鸿沟，我们设计了共生型多模态自注意力机制与统一微调算法。这种协同使生成任务能够提升真实性识别的可解释性，同时真实性准则引导更高保真度图像的生成。此外，我们引入一种检测器引导的生成对齐机制，以促进无缝信息交换。在多个数据集上的大量实验表明，我们的方法达到了最先进性能。代码：\href{https://github.com/Zhangyr2022/UniGenDet}{https://github.com/Zhangyr2022/UniGenDet}。