  Recent advances in adversarial robustness rely on an abundant set of training
data, where using external or additional datasets has become a common setting.
However, in real life, the training data is often kept private for security and
privacy issues, while only the pretrained weight is available to the public. In
such scenarios, existing methods that assume accessibility to the original data
become inapplicable. Thus we investigate the pivotal problem of data-free
adversarial robustness, where we try to achieve adversarial robustness without
accessing any real data. Through a preliminary study, we highlight the severity
of the problem by showing that robustness without the original dataset is
difficult to achieve, even with similar domain datasets. To address this issue,
we propose DataFreeShield, which tackles the problem from two perspectives:
surrogate dataset generation and adversarial training using the generated data.
Through extensive validation, we show that DataFreeShield outperforms
baselines, demonstrating that the proposed method sets the first entirely
data-free solution for the adversarial robustness problem.
