Improving Adversarial Transferability via Neuron Attribution-Based Attacks. (arXiv:2204.00008v1 [cs.LG])

Deep neural networks (DNNs) are known to be vulnerable to adversarial
examples. It is thus imperative to devise effective attack algorithms to
identify the deficiencies of DNNs beforehand in security-sensitive
applications. To efficiently tackle the black-box setting where the target
model's particulars are unknown, feature-level transfer-based attacks propose
to contaminate the intermediate feature outputs of local models, and then
directly employ the crafted adversarial samples to attack the target model. Due
to the transferability of features, feature-level attacks have shown …

adversarial attacks attribution lg