This study delves into the enhancement of Under-Display Camera (UDC) image restoration models, focusing on their robustness against adversarial attacks. Despite its innovative approach to seamless display integration, UDC technology faces unique image degradation challenges exacerbated by the susceptibility to adversarial perturbations. Our research initially conducts an in-depth robustness evaluation of deep-learning-based UDC image restoration models by employing several white-box and black-box attacking methods. This evaluation is pivotal in understanding the vulnerabilities of current UDC image restoration techniques. Following the assessment, we introduce a defense framework integrating adversarial purification with subsequent fine-tuning processes. First, our approach employs diffusion-based adversarial purification, effectively neutralizing adversarial perturbations. Then, we apply the fine-tuning methodologies to refine the image restoration models further, ensuring that the quality and fidelity of the restored images are maintained. The effectiveness of our proposed approach is validated through extensive experiments, showing marked improvements in resilience against typical adversarial attacks.

翻译：本研究深入探讨了屏下摄像头(UDC)图像恢复模型的增强问题，重点关注其对抗对抗性攻击的鲁棒性。尽管UDC技术在实现无缝显示集成方面具有创新性，但其面临的独特图像退化问题因对抗性扰动的敏感性而进一步加剧。我们首先通过采用多种白盒与黑盒攻击方法，对基于深度学习的UDC图像恢复模型进行了深入的鲁棒性评估。这一评估对于理解当前UDC图像恢复技术的脆弱性至关重要。在评估之后，我们提出了一种将对抗净化与后续微调过程相结合的防御框架。首先，我们的方法采用基于扩散的对抗净化，有效消除对抗性扰动。随后，我们应用微调技术进一步优化图像恢复模型，确保恢复图像的质量与保真度得以维持。通过大量实验验证了所提方法的有效性，结果显示在应对典型对抗性攻击时具有显著的鲁棒性提升。