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 attacks on deep neural networks traditionally rely on aconstrained optimization paradigm, where an optimization procedure is used toobtain a single adversarial perturbation for a given input example. In thiswork we frame the problem as learning a distribution of adversarialperturbations, enabling us to generate diverse adversarial distributions givenan unperturbed input. We show that this framework is domain-agnostic in thatthe same framework can be employed to attack different input domains withminimal modification. Across three diverse domains---images, text, andgraphs---our approach generates whitebox attacks with success rates that arecompetitive with or superior to existing approaches, with a newstate-of-the-art achieved in the graph domain. Finally, we demonstrate that ourframework can efficiently generate a diverse set of attacks for a single giveninput, and is even capable of attacking \textit{unseen} test instances in azero-shot manner, exhibiting attack generalization.

Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling