The rapid advancement of generators (e.g., StyleGAN, Midjourney, DALL-E) has produced highly realistic synthetic images, posing significant challenges to digital media authenticity. These generators are typically based on a few core architectural families, primarily Generative Adversarial Networks (GANs) and Diffusion Models (DMs). A critical vulnerability in current forensics is the failure of detectors to achieve cross-generator generalization, especially when crossing architectural boundaries (e.g., from GANs to DMs). We hypothesize that this gap stems from fundamental differences in the artifacts produced by these \textbf{distinct architectures}. In this work, we provide a theoretical analysis explaining how the distinct optimization objectives of the GAN and DM architectures lead to different manifold coverage behaviors. We demonstrate that GANs permit partial coverage, often leading to boundary artifacts, while DMs enforce complete coverage, resulting in over-smoothing patterns. Motivated by this analysis, we propose the \textbf{Tri}archy \textbf{Detect}or (TriDetect), a semi-supervised approach that enhances binary classification by discovering latent architectural patterns within the "fake" class. TriDetect employs balanced cluster assignment via the Sinkhorn-Knopp algorithm and a cross-view consistency mechanism, encouraging the model to learn fundamental architectural distincts. We evaluate our approach on two standard benchmarks and three in-the-wild datasets against 13 baselines to demonstrate its generalization capability to unseen generators.

翻译：生成器（如StyleGAN、Midjourney、DALL-E）的快速发展已能产生高度逼真的合成图像，对数字媒体真实性构成重大挑战。这些生成器通常基于少数核心架构家族，主要是生成对抗网络（GANs）和扩散模型（DMs）。当前取证技术的一个关键缺陷是检测器无法实现跨生成器的泛化，尤其是在跨越架构边界时（例如从GANs到DMs）。我们假设这一差距源于这些不同架构产生的伪影存在根本性差异。在本研究中，我们通过理论分析解释了GAN和DM架构的不同优化目标如何导致不同的流形覆盖行为。我们证明GANs允许部分覆盖，常导致边界伪影，而DMs强制完全覆盖，产生过度平滑模式。基于此分析，我们提出Triarchy检测器（TriDetect），这是一种半监督方法，通过发现“虚假”类别内的潜在架构模式来增强二元分类。TriDetect采用Sinkhorn-Knopp算法实现平衡聚类分配，并引入跨视图一致性机制，促使模型学习基本的架构差异。我们在两个标准基准和三个真实场景数据集上评估了该方法，对比了13个基线模型，以证明其对未见生成器的泛化能力。