  Training a high-performance deep neural network requires large amounts of
data and computational resources. Protecting the intellectual property (IP) and
commercial ownership of a deep model is challenging yet increasingly crucial. A
major stream of watermarking strategies implants verifiable backdoor triggers
by poisoning training samples, but these are often unrealistic due to data
privacy and safety concerns and are vulnerable to minor model changes such as
fine-tuning. To overcome these challenges, we propose a safe and robust
backdoor-based watermark injection technique that leverages the diverse
knowledge from a single out-of-distribution (OoD) image, which serves as a
secret key for IP verification. The independence of training data makes it
agnostic to third-party promises of IP security. We induce robustness via
random perturbation of model parameters during watermark injection to defend
against common watermark removal attacks, including fine-tuning, pruning, and
model extraction. Our experimental results demonstrate that the proposed
watermarking approach is not only time- and sample-efficient without training
data, but also robust against the watermark removal attacks above.
