Randomized smoothing-based certification is an effective approach for obtaining robustness certificates of deep neural networks (DNNs) against adversarial attacks. This method constructs a smoothed DNN model and certifies its robustness through statistical sampling, but it is computationally expensive, especially when certifying with a large number of samples. Furthermore, when the smoothed model is modified (e.g., quantized or pruned), certification guarantees may not hold for the modified DNN, and recertifying from scratch can be prohibitively expensive. We present the first approach for incremental robustness certification for randomized smoothing, IRS. We show how to reuse the certification guarantees for the original smoothed model to certify an approximated model with very few samples. IRS significantly reduces the computational cost of certifying modified DNNs while maintaining strong robustness guarantees. We experimentally demonstrate the effectiveness of our approach, showing up to 3x certification speedup over the certification that applies randomized smoothing of the approximate model from scratch.

翻译：基于随机平滑的认证是一种为深度神经网络（DNN）提供对抗攻击鲁棒性证书的有效方法。该方法构建平滑后的DNN模型，并通过统计采样来证明其鲁棒性，但计算成本较高，尤其是在需要大量样本进行认证时。此外，当平滑模型被修改（如量化或剪枝）后，原有的认证保证可能不再适用于修改后的DNN，而从头开始重新认证的成本过高。我们提出了首个面向随机平滑的增量式鲁棒性认证方法IRS。该方法展示了如何复用原始平滑模型的认证保证，以极少的样本对近似模型进行认证。IRS在保持强鲁棒性保证的同时，显著降低了修改后DNN的认证计算成本。实验结果表明，与对近似模型从头应用随机平滑的认证方法相比，我们的方法可实现高达3倍的认证加速。