Steering the Flow: Inverting Face Recognition Models via Gradient-Guided Flow Matching
Score
76首次将流匹配与渐进式梯度引导结合用于模型反演，方法新颖且实验证据充分。
论文结构完整，公式与实验设计严谨，结果详实，但未知评审状态。
对评估人脸识别系统安全性具有现实意义，但攻击场景限于白盒假设。
作为arXiv最新论文，紧跟生成模型安全研究前沿，具有高度时效性。
arXiv论文虽未经同行评审，但来自AI/CL/LG范畴且方法可复现，可信度较高。
Base 84 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The method is highly innovative with rigorous experimental design and comprehensive results, offering a new paradigm for white-box model inversion.
AI commentary
「This paper is worth attention because it cleverly extends the hot Flow Matching from 'generating images' to 'inverting face recognition', offering a fresh angle for model security research. For those studying AI safety and privacy, it provides a key reference for understanding the upper bound of white-box attacks.」
Quick overview
Model Inversion Attacks (MIAs) aim to reconstruct representative training samples of target identities from face recognition models, exposing critical security vulnerabilities. Existing methods rely on indirect or highly stochastic guidance, making it difficult to stably optimize generation trajectories. This paper proposes SFMI, a two-stage white-box inversion method: Stage I pre-trains a generic unconditional Flow Matching model to encode the human face manifold as a robust prior; Stage II introduces a Progressive Guidance Scheduler (PGS) that injects time-dependent target-specific gradients during sampling. By backpropagating through the target model to obtain gradients from intermediate generated states, PGS adaptively steers the generative flow from random noise toward high-density regions of the target class. On CelebA with ArcFace target, SFMI achieves ACC 0.9248, FID 22.61, LPIPS 0.3874, and competitive state-of-the-art performance across multiple target models in white-box settings.
- Proposes SFMI, reformulating model inversion as a trajectory-steering task with two stages: flow matching prior learning and progressive guidance scheduling.
- The Progressive Guidance Scheduler obtains target gradients via backpropagation from intermediate generated states, enabling stable and adaptive attack guidance.
- On CelebA with ArcFace target, SFMI achieves ACC of 0.9248, FID of 22.61, and LPIPS of 0.3874.
- Demonstrates competitive attack success and visual fidelity across multiple white-box target models.
Full text
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