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：本研究深入探讨了提升Under-Display Camera（UDC）图像复原模型鲁棒性的方法，重点关注其对抗对抗性攻击的能力。尽管UDC技术在实现无缝显示集成方面具有创新性，但其面临着独特的图像退化挑战，且易受对抗性扰动影响，加剧了这些问题。我们的研究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图像复原模型进行了深入的鲁棒性评估。该评估对于理解当前UDC图像复原技术的脆弱性至关重要。评估之后，我们提出了一种将对抗性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性净化，有效中和对抗性扰动。随后，我们应用微调方法进一步优化图像复原模型，确保复原图像的质量与保真度得以维持。通过大量实验验证了我们所提方法的有效性，结果显示其在抵御典型对抗性攻击方面的韧性有显著提升。