Towards Deep Learning Models Resistant to Adversarial Attacks — mentioned by Imnimo

Sat, 20 Feb 2021 20:40:53 GMT

I don't really think this is true for two reasons. First, the app doesn't add anything that someone training a classifier couldn't just do themselves. If I want to train my network against adversarial inputs, I can just generate them myself at training time on my own training data - it's not particularly helpful for me to have a bunch adversarially-perturbed but unlabeled images. Second, and more importantly, it's not sufficient to just train on adversarial examples taken from another network. You might learn to be robust against the specific weaknesses of that other network, but your new…

Paper and discussion history. A mention is not an endorsement.

Towards Deep Learning Models Resistant to Adversarial Attacks — mentioned by logane

Wed, 01 Nov 2017 19:37:53 GMT

Another author here - adversarial training is not sufficient to protect against white-box attacks but it seems to be the best method we have so far (https://arxiv.org/abs/1706.06083). It seems that all current state of the art architectures are vulnerable to adversarial examples; to the best of my knowledge there are no image classification networks that researchers have failed to reliably produce adversarial examples for.

Paper and discussion history. A mention is not an endorsement.

Towards Deep Learning Models Resistant to Adversarial Attacks · HackerPapers discussions

Towards Deep Learning Models Resistant to Adversarial Attacks — mentioned by Imnimo

Towards Deep Learning Models Resistant to Adversarial Attacks — mentioned by logane