Intercepting intermediate features in split neural network inference lets black-box attackers build surrogate models whose adversarial examples transfer to the target far more often, e.g., 96% versus 61% success in one setting.
Practical black-box attacks against machine learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning
Intercepting intermediate features in split neural network inference lets black-box attackers build surrogate models whose adversarial examples transfer to the target far more often, e.g., 96% versus 61% success in one setting.