Abstract and subjects
Adversarial examples for neural network image classifiers are known to be
transferable: examples optimized to be misclassified by a source classifier are
often misclassified as well by classifiers with different architectures.
However, targeted adversarial examples -- optimized to be classified as a
chosen target class -- tend to be less transferable between architectures.
While prior research on constructing transferable targeted attacks has focused
on improving the optimization procedure, in this work we examine the role of
the source classifier. Here, we show that training the source classifier to be
"slightly robust" -- that is, robust to small-magnitude adversarial examples --
substantially improves the transferability of class-targeted and
representation-targeted adversarial attacks, even between architectures as
different as convolutional neural networks and transformers. The results we
present provide insight into the nature of adversarial examples as well as the
mechanisms underlying so-called "robust" classifiers.