Incremental Randomized Smoothing Certification

arXiv:2305.19521v2 Announce Type: replace
Abstract: 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. …

abstract adversarial adversarial attacks arxiv attacks certification cs.cr cs.lg cs.pl dnn incremental networks neural networks robustness samples sampling statistical through type