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

A wide variety of works have explored the reason for the existence ofadversarial examples, but there is no consensus on the explanation. We proposeto treat the DNN logits as a vector for feature representation, and exploitthem to analyze the mutual influence of two independent inputs based on thePearson correlation coefficient (PCC). We utilize this vector representation tounderstand adversarial examples by disentangling the clean images andadversarial perturbations, and analyze their influence on each other. Ourresults suggest a new perspective towards the relationship between images anduniversal perturbations: Universal perturbations contain dominant features, andimages behave like noise to them. This feature perspective leads to a newmethod for generating targeted universal adversarial perturbations using randomsource images. We are the first to achieve the challenging task of a targeteduniversal attack without utilizing original training data. Our approach using aproxy dataset achieves comparable performance to the state-of-the-art baselineswhich utilize the original training dataset.

Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations