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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Neural Network classifiers have been used successfully in a wide range ofapplications. However, their underlying assumption of attack free environmenthas been defied by adversarial examples. Researchers tried to develop defenses;however, existing approaches are still far from providing effective solutionsto this evolving problem. In this paper, we design a generative adversarial net(GAN) based zero knowledge adversarial training defense, dubbed ZK-GanDef,which does not consume adversarial examples during training. Therefore,ZK-GanDef is not only efficient in training but also adaptive to newadversarial examples. This advantage comes at the cost of small degradation intest accuracy compared to full knowledge approaches. Our experiments show thatZK-GanDef enhances test accuracy on adversarial examples by up-to 49.17%compared to zero knowledge approaches. More importantly, its test accuracy isclose to that of the state-of-the-art full knowledge approaches (maximumdegradation of 8.46%), while taking much less training time.

ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks