基于人脸识别的对抗样本生成算法研究

Research on Adversarial Sample Generation Algorithm Based on Face Recognition

摘要: 随着互联网的广泛应用，各种智能化系统的出现极大地提升了人们的生活质量和工作效率。在众多智能化系统中，人脸识别技术的应用最为广泛。人脸识别系统虽然在众多领域发挥了重要作用，但仍面临许多挑战。例如人脸识别系统容易被恶意攻击，亟待需要安全性测试。现有对抗样本存在攻击性弱，视觉效果差问题。本文提出的模型通过StyleNet风格–内容解耦编码、FusionNet多层次风格注入，辅以变形交叉注意力、频域融合和自适应融合金字塔等创新模块，实现了妆容风格从参考人脸到目标人脸的自然迁移。数学推导和实现细节表明，这些模块有效解决了妆容迁移中的局部特征对齐、色彩纹理分离控制、区域平滑融合等难点问题。本文提出的AdversarialMakeup模型在妆容迁移与隐私攻击的核心指标上取得了卓越的综合性能。定量评估表明，该方法在关键指标上达到平均攻击成功率(ASR(avg)) 0.15，同时保持了优异的视觉质量(LPIPS为0.34，SSIM为0.90)和颜色分布一致性(HistDist为0.095)。

Abstract: With the wide application of the Internet, the emergence of various intelligent systems has greatly improved people’s quality of life and work efficiency. Among the many intelligent systems, facial recognition technology is the most widely used. Although facial recognition systems have played an important role in many fields, they still face many challenges. For example, facial recognition systems are vulnerable to malicious attacks and urgently need security testing. Existing adversarial samples have weak attack capabilities and poor visual effects. The model proposed in this paper achieves natural transfer of makeup style from the reference face to the target face through StyleNet style-content decoupling encoding, FusionNet multi-level style injection, and innovative modules such as deformable cross-attention, frequency domain fusion, and adaptive fusion pyramid. Mathematical derivations and implementation details show that these modules effectively solve the difficult problems in makeup transfer, such as local feature alignment, color texture separation control, and regional smooth fusion. The AdversarialMakeup model proposed in this paper has achieved outstanding comprehensive performance in the core indicators of makeup transfer and privacy attack. Quantitative evaluation shows that this method achieves an average attack success rate (ASR(avg)) of 0.15 on key indicators, while maintaining excellent visual quality (LPIPS of 0.34, SSIM of 0.90) and color distribution consistency (HistDist of 0.095).