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

Detection of contrast adjustments in the presence of JPEG postprocessing isknown to be a challenging task. JPEG post processing is often appliedinnocently, as JPEG is the most common image format, or it may correspond to alaundering attack, when it is purposely applied to erase the traces ofmanipulation. In this paper, we propose a CNN-based detector for genericcontrast adjustment, which is robust to JPEG compression. The proposed systemrelies on a patch-based Convolutional Neural Network (CNN), trained todistinguish pristine images from contrast adjusted images, for some selectedadjustment operators of different nature. Robustness to JPEG compression isachieved by training the CNN with JPEG examples, compressed over a range ofQuality Factors (QFs). Experimental results show that the detector works verywell and scales well with respect to the adjustment type, yielding very goodperformance under a large variety of unseen tonal adjustments.

CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing