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

The works in literature classifying natural and computer generated images aremostly designed as binary tasks either considering natural images versuscomputer graphics images only or natural images versus GAN generated imagesonly, but not natural images versus both classes of the generated images. Also,even though this forensic classification task of distinguishing natural andcomputer generated images gets the support of the new convolutional neuralnetworks and transformer based architectures that can give remarkableclassification accuracies, they are seen to fail over the images that haveundergone some post-processing operations usually performed to deceive theforensic algorithms, such as JPEG compression, gaussian noise, etc. This workproposes a robust approach towards distinguishing natural and computergenerated images including both, computer graphics and GAN generated imagesusing a fusion of two vision transformers where each of the transformernetworks operates in different color spaces, one in RGB and the other in YCbCrcolor space. The proposed approach achieves high performance gain when comparedto a set of baselines, and also achieves higher robustness and generalizabilitythan the baselines. The features of the proposed model when visualized are seento obtain higher separability for the classes than the input image features andthe baseline features. This work also studies the attention map visualizationsof the networks of the fused model and observes that the proposed methodologycan capture more image information relevant to the forensic task of classifyingnatural and generated images.

A Robust Approach Towards Distinguishing Natural and Computer Generated Images using Multi-Colorspace fused and Enriched Vision Transformer