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

Recent developments in computer vision and machine learning have made itpossible to create realistic manipulated videos of human faces, raising theissue of ensuring adequate protection against the malevolent effects unlockedby such capabilities. In this paper we propose local image features that areshared across manipulated regions are the key element for the automaticdetection of manipulated face images. We also design a lightweight architecturewith the correct structural biases for extracting such features and derive amultitask training scheme that consistently outperforms image class supervisionalone. The trained networks achieve state-of-the-art results in theFaceForensics++ dataset using significantly reduced number of parameters andare shown to work well in detecting fully generated face images.

Extracting deep local features to detect manipulated images of human faces