Recent work has proposed neural network pruning techniques to reduce the size of a network while preserving robustness against adversarial examples, i.e., well-crafted inputs inducing a misclassification. These methods, which we refer to as adversarial pruning methods, involve complex and articulated designs, making it difficult to analyze the differences and establish a fair and accurate comparison. In this work, we overcome these issues by surveying current adversarial pruning methods and proposing a novel taxonomy to categorize them based on two main dimensions: the pipeline, defining when to prune; and the specifics, defining how to prune. We then highlight the limitations of current empirical analyses and propose a novel, fair evaluation benchmark to address them. We finally conduct an empirical re-evaluation of current adversarial pruning methods and discuss the results, highlighting the shared traits of top-performing adversarial pruning methods, as well as common issues. We welcome contributions in our publicly-available benchmark at https://github.com/pralab/AdversarialPruningBenchmark

本研究解决了现有对抗性剪枝方法难以比较和分析的问题，通过对当前方法的调查和提出基于修剪流程和具体技术的新分类法来提升理解。此外，研究提出了一个新的基准来评估这些方法，揭示了表现最佳的对抗性剪枝技术的共同特征及其面临的普遍问题。

对抗性剪枝：对对抗性鲁棒性剪枝方法的调查与基准