Adversarial attacks have verified the existence of the vulnerability of neural networks. By adding small perturbations to a benign example, adversarial attacks successfully generate adversarial examples that lead misclassification of deep learning models. More importantly, an adversarial example generated from a specific model can also deceive other models without modification. We call this phenomenon ``transferability". Here, we analyze the relationship between transferability and input transformation with additive noise by mathematically proving that the modified optimization can produce more transferable adversarial examples.
@article{arxiv.2206.09075,
title = {Comment on Transferability and Input Transformation with Additive Noise},
author = {Hoki Kim and Jinseong Park and Jaewook Lee},
journal= {arXiv preprint arXiv:2206.09075},
year = {2022}
}