A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems (2504.21420v1)

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.

An Efficient Test Suite for Evaluating Robustness in Face Recognition Systems

Modern face recognition systems are increasingly being utilized in various security-centric applications, necessitating the development of rigorous methods to evaluate their robustness. Traditional approaches to assessing the robustness of face recognition systems, such as empirical testing through adversarial attacks or theoretical verification via measures like the Lipschitz constant, often have limitations in terms of computational efficiency and applicability across different transformations. The paper introduces RobFace, a novel test suite designed to provide a comprehensive, efficient, and system-agnostic evaluation of face recognition robustness by leveraging an optimized set of transferable adversarial face images.

Overview of Methods

RobFace presents an "old-school" but under-explored approach to robustness evaluation through systematic testing rather than on-the-fly adversarial example generation. The central concept involves the construction and deployment of a pre-optimized test suite, where each test contains benign and adversarially perturbed image pairs. This suite is system-agnostic, allowing it to be used across different face recognition models without system-specific adjustments. This property is critical, given the variety of architectures and training methodologies in use today.

Key Components and Results

1. Transferable Adversarial Examples: The test suite employs adversarial examples with demonstrated transferability across models. This mitigates the need for iterative adversarial searches for each face recognition system, a task that can be computationally expensive.
2. Comprehensive Perturbation Coverage: RobFace evaluates over multiple perturbation dimensions, not just limited to traditional $L^p$-norm constraints but also including realistic transformations such as wearing glasses, facial accessories, and lighting changes. This extensive coverage ensures that the robustness measures provided by the suite account for real-world scenarios where such perturbations are likely.
3. Efficiency and Scalability: The robustness evaluation using RobFace is shown to be approximately 200 times faster than traditional empirical attacks like PGD, without loss of accuracy in robustness estimation. This significant efficiency gain arises from the pre-optimization of the test suite, which circumvents the time-intensive iterative search and gradient computation required in traditional methods.
4. Robustness Evaluation Consistency: Empirical evaluations indicate that the robustness estimates from RobFace are highly correlated with those obtained through established adversarial accuracy methods. Correlations typically range from 0.9 to 0.99 across various types of perturbations, validating the effectiveness of the proposed testing model.

Implications and Future Directions

RobFace represents a shift in how robustness evaluations can be conducted, offering both practical and operational advantages. Its comprehensive perturbation evaluation aligns with the growing need for diverse robustness testing regimes as the deployment of face recognition technologies expands globally. Furthermore, this approach highlights the potential for pre-optimized test suites to generalize across unseen systems, suggesting a new direction for efficiency in robustness evaluation methodologies.

Future work can extend RobFace to incorporate an even broader range of perturbations and adapt the methodology for robustness evaluation in other domains and modalities of neural networks. As adversarial attacks continue to evolve, maintaining a test suite that systematically covers emerging attack vectors will be crucial. Additionally, strategies to prevent systems from retroactively overfitting to specific test suites, an issue carefully addressed in RobFace, will remain an ongoing challenge.

Overall, RobFace sets the stage for efficient, scalable, and robust testing infrastructure that is adaptable to diverse applications of machine learning systems in security-critical environments.