  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.
