English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Adversarial examples for neural network image classifiers are known to betransferable: examples optimized to be misclassified by a source classifier areoften misclassified as well by classifiers with different architectures.However, targeted adversarial examples -- optimized to be classified as achosen target class -- tend to be less transferable between architectures.While prior research on constructing transferable targeted attacks has focusedon improving the optimization procedure, in this work we examine the role ofthe 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 andrepresentation-targeted adversarial attacks, even between architectures asdifferent as convolutional neural networks and transformers. The results wepresent provide insight into the nature of adversarial examples as well as themechanisms underlying so-called "robust" classifiers.

A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks