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Evaluation Metrics of Adversarial Attacks on Face Recognition

Introduction to Adversarial Attacks on Face Recognition

Face recognition (FR) systems, powered by deep learning, have become integral to various applications, from social media photo tagging to automated border control. However, these systems are vulnerable to adversarial attacks, where slight perturbations to input images can lead to incorrect predictions . Understanding and evaluating these attacks is crucial for improving the robustness of FR systems.

Types of Adversarial Attacks

Cross-Resolution Adversarial Attacks

Adversarial attacks can exploit different image resolutions to deceive FR systems. Studies have shown that attacks based on L1, L2, and L∞ metrics can significantly impact the performance of FR systems across various resolutions. These attacks are particularly effective in cross-resolution scenarios, which are common in biometric and forensic applications.

Spatial Mutable Adversarial Patch (SMAP)

The SMAP method generates dynamic patches that can be injected into facial images. This approach optimizes the texture, position, and shape of the patch to maximize its adversarial impact. The evaluation of SMAP under black-box settings has shown improved attack performance and transferability across different FR models.

Physical-World Attacks

Physical-world attacks, such as sticker-based methods, pose a significant threat to FR systems. The PadvFace framework models various physical conditions to enhance the robustness of these attacks. The Curriculum Adversarial Attack (CAA) algorithm further adapts adversarial stickers to environmental variations, demonstrating high success rates in both dodging and impersonation attacks.

Natural Makeup Attacks

The Adv-Eye method uses natural eye makeup to create adversarial samples. This approach balances imperceptibility and attack capability, achieving high success rates in black-box settings. The method involves generating and blending eyeshadow on the orbital region, significantly improving the visual quality and attack success rates.

Semantic Adversarial Attacks

Semantic Adversarial Attacks (SAA) manipulate significant facial attributes to deceive FR systems. The SAA-StarGAN method predicts and alters the most impactful attributes, achieving high success rates in both white-box and black-box settings. This approach outperforms traditional transformation-based and gradient-based attacks.

Evaluation Metrics for Adversarial Attacks

Attack Success Rate (ASR)

ASR measures the percentage of successful adversarial attacks. High ASR indicates the effectiveness of the attack method in deceiving the FR system.

Transferability

Transferability assesses the ability of adversarial examples to deceive different FR models. High transferability is crucial for black-box attacks, where the attacker has no access to the target model .

Imperceptibility

Imperceptibility evaluates how natural the adversarial perturbations appear to human observers. Methods like Adv-Eye focus on maintaining high visual quality while achieving successful attacks.

Robustness Across Resolutions

Evaluating the impact of adversarial attacks across different image resolutions helps in understanding the resilience of FR systems in real-world scenarios. Cross-resolution training approaches can enhance the robustness of FR models.

Environmental Variations

Physical-world attacks must consider environmental variations such as lighting and angle. The PadvFace framework and CAA algorithm address these variations to improve the robustness of physical attacks.

Conclusion

Adversarial attacks pose a significant threat to the security and reliability of face recognition systems. Evaluating these attacks using metrics like ASR, transferability, imperceptibility, and robustness across resolutions and environmental conditions is essential for developing more resilient FR systems. Future research should focus on enhancing the robustness of FR models against these sophisticated adversarial techniques.