Fake Image Detection (FID), aiming at unified detection across four image forensic subdomains, is critical in real-world forensic scenarios. Compared with ensemble approaches, monolithic FID models are theoretically more promising, but to date, consistently yield inferior performance in practice. In this work, by discovering the ``heterogeneous phenomenon'', which is the intrinsic distinctness of artifacts across subdomains, we diagnose the cause of this underperformance for the first time: the collapse of the artifact feature space driven by such phenomenon. The core challenge for developing a practical monolithic FID model thus boils down to the ``unified-yet-discriminative" reconstruction of the artifact feature space. To address this paradoxical challenge, we hypothesize that high-level semantics can serve as a structural prior for the reconstruction, and further propose Semantic-Induced Constrained Adaptation (SICA), the first monolithic FID paradigm. Extensive experiments on our OpenMMSec dataset demonstrate that SICA outperforms 15 state-of-the-art methods and reconstructs the target unified-yet-discriminative artifact feature space in a near-orthogonal manner, thus firmly validating our hypothesis. The code and dataset are available at:https: //github.com/scu-zjz/SICA_OpenMMSec.

翻译：虚假图像检测旨在实现跨四个图像取证子领域的统一检测，在实际取证场景中至关重要。与集成方法相比，统一的虚假图像检测模型在理论上更具前景，但迄今为止在实践中始终表现欠佳。本研究通过发现"异构现象"——即不同子领域间伪造痕迹的内在差异性，首次诊断了这种性能不足的根源：由该现象导致的伪造特征空间坍缩。因此，开发实用统一虚假图像检测模型的核心挑战可归结为"统一且可区分"的伪造特征空间重构。为应对这一矛盾性挑战，我们提出高层语义可作为重构的结构性先验，并进一步提出语义引导约束适应方法——首个统一的虚假图像检测范式。在我们构建的OpenMMSec数据集上进行的大量实验表明，SICA在性能上超越了15种先进方法，并以近似正交的方式重构出目标所需的统一且可区分伪造特征空间，从而有力验证了我们的假设。代码与数据集已开源：https://github.com/scu-zjz/SICA_OpenMMSec。