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/

Zendy Plus

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

Zendy Tools

Zendy Open

Google's Vision API analyses images and provides a variety of outputpredictions, one such type is context-based labelling. In this paper, it isshown that adversarial examples that cause incorrect label prediction andspoofing can be generated by rotating the images. Due to the black-boxed natureof the API, a modular context-based pre-processing pipeline is proposedconsisting of a Res-Net50 model, that predicts the angle by which the imagemust be rotated to correct its orientation. The pipeline successfully performsthe correction whilst maintaining the image's resolution and feeds it to theAPI which generates labels similar to the original correctly oriented image andusing a Percentage Error metric, the performance of the corrected images ascompared to its rotated counter-parts is found to be significantly higher.These observations imply that the API can benefit from such a pre-processingpipeline to increase robustness to rotational perturbances.

Countering Inconsistent Labelling by Google's Vision API for Rotated Images