Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness
<|reference_start|>Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness: 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<|reference_end|>
@article{piras2024adversarial,
    title={Adversarial Pruning: A Survey and Benchmark of Pruning Methods for
  Adversarial Robustness},
    author={Giorgio Piras, Maura Pintor, Ambra Demontis, Battista Biggio, Giorgio
  Giacinto, Fabio Roli},
    journal={arXiv preprint arXiv:2409.01249},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01249},
    primaryClass={cs.LG cs.CR cs.CV}
}