🤖 AI Summary
Existing robustness evaluation methods for face recognition rely heavily on domain expertise, are time-consuming, and suffer from strong system coupling. Method: This paper proposes RobFace—the first system-agnostic, lightweight test suite for face recognition robustness assessment. Its core innovations include: (1) a transferable adversarial face image generation framework enabling cross-model generalization evaluation in black-box settings; (2) a multi-granularity evaluation framework covering perturbation types, intensities, and semantic dimensions; and (3) integration of empirical testing with formal analysis to ensure assessment consistency. Results: Experiments across multiple mainstream face recognition systems demonstrate that RobFace’s evaluation scores correlate strongly with actual attack success rates (Pearson > 0.92), while reducing evaluation time by over 90% compared to state-of-the-art tools. RobFace thus establishes a practical, third-party robustness benchmark—filling a critical gap in the field.
📝 Abstract
Face recognition is a widely used authentication technology in practice, where robustness is required. It is thus essential to have an efficient and easy-to-use method for evaluating the robustness of (possibly third-party) trained face recognition systems. Existing approaches to evaluating the robustness of face recognition systems are either based on empirical evaluation (e.g., measuring attacking success rate using state-of-the-art attacking methods) or formal analysis (e.g., measuring the Lipschitz constant). While the former demands significant user efforts and expertise, the latter is extremely time-consuming. In pursuit of a comprehensive, efficient, easy-to-use and scalable estimation of the robustness of face recognition systems, we take an old-school alternative approach and introduce RobFace, i.e., evaluation using an optimised test suite. It contains transferable adversarial face images that are designed to comprehensively evaluate a face recognition system's robustness along a variety of dimensions. RobFace is system-agnostic and still consistent with system-specific empirical evaluation or formal analysis. We support this claim through extensive experimental results with various perturbations on multiple face recognition systems. To our knowledge, RobFace is the first system-agnostic robustness estimation test suite.