Deep neural networks (DNNs) are increasingly used in critical applications such as identity authentication and autonomous driving, where robustness against adversarial attacks is crucial. These attacks can exploit minor perturbations to cause significant prediction errors, making it essential to enhance the resilience of DNNs. Traditional defense methods often rely on access to detailed model information, which raises privacy concerns, as model owners may be reluctant to share such data. In contrast, existing black-box defense methods fail to offer a universal defense against various types of adversarial attacks. To address these challenges, we introduce DUCD, a universal black-box defense method that does not require access to the target model's parameters or architecture. Our approach involves distilling the target model by querying it with data, creating a white-box surrogate while preserving data privacy. We further enhance this surrogate model using a certified defense based on randomized smoothing and optimized noise selection, enabling robust defense against a broad range of adversarial attacks. Comparative evaluations between the certified defenses of the surrogate and target models demonstrate the effectiveness of our approach. Experiments on multiple image classification datasets show that DUCD not only outperforms existing black-box defenses but also matches the accuracy of white-box defenses, all while enhancing data privacy and reducing the success rate of membership inference attacks.

本研究针对深度神经网络在身份验证和自动驾驶等关键应用中对抗攻击的脆弱性问题，提出了一种新的通用黑盒防御方法（DUCD），该方法不需访问目标模型的参数或结构。在多个图像分类数据集上的实验表明，DUCD不仅超越了现有的黑盒防御，还与白盒防御的准确性相当，同时增强了数据隐私并降低了成员推断攻击的成功率。

保护隐私的通用对抗防御方法用于黑盒模型