想了解人脸识别模型漏洞？这篇用流匹配做模型反演，效果比旧方法好，数字摆在那，值得一看。
本文提出SFMI，一种两阶段白盒模型反演方法，将反演重构为轨迹引导任务。第一阶段预训练无条件流匹配模型编码人脸流形作为先验；第二阶段通过渐进引导调度器注入时间相关梯度，沿生成轨迹引导至目标身份高密度区域。在CelebA数据集、ArcFace目标上，SFMI达到ACC 0.9248、FID 22.61、LPIPS 0.3874，优于现有方法。
Steering the Flow: Inverting Face Recognition Models via Gradient-Guided Flow Matching
Model Inversion Attacks (MIAs) aim to reconstruct representative training samples of target identities from face recognition models, exposing critical security vulnerabilities. Existing methods typically rely on indirect guidance or highly stochastic guidance, making it difficult to stably optimize generation trajectories toward target facial images. In this paper, we propose Steering Flow Model Inversion (SFMI), a novel two-stage white-box model inversion method that reformulates inversion as a trajectory-steering task. Specifically, Step I, Learning a Generic Flow Matching Prior, pre-trains a generic unconditional Flow Matching model to encode the manifold of human faces as a robust prior. Step II, Attacking with Progressive Guidance Scheduler (PGS), injects time-dependent target-specific gradients during sampling. By backpropagating through the target model to obtain gradients from intermediate generated states, PGS progressively injects adaptive guidance signals into the vector field. This process effectively steers the current generative flow from random noise toward the high-density regions of the target class. Under an identity-disjoint cross-evaluation setting using the CelebA dataset, SFMI achieves an ACC of 0.9248, an FID of 22.61, and an LPIPS of 0.3874 on the ArcFace target. Extensive experiments on multiple target models demonstrate that SFMI achieves competitive state-of-the-art performance in attack success and visual fidelity under the evaluated white-box protocol.