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

With the rapid development of deep generative models (such as GenerativeAdversarial Networks and Diffusion models), AI-synthesized images are now ofsuch high quality that humans can hardly distinguish them from pristine ones.Although existing detection methods have shown high performance in specificevaluation settings, e.g., on images from seen models or on images withoutreal-world post-processing, they tend to suffer serious performance degradationin real-world scenarios where testing images can be generated by more powerfulgeneration models or combined with various post-processing operations. Toaddress this issue, we propose a Global and Local Feature Fusion (GLFF)framework to learn rich and discriminative representations by combiningmulti-scale global features from the whole image with refined local featuresfrom informative patches for AI synthesized image detection. GLFF fusesinformation from two branches: the global branch to extract multi-scalesemantic features and the local branch to select informative patches fordetailed local artifacts extraction. Due to the lack of a synthesized imagedataset simulating real-world applications for evaluation, we further create achallenging fake image dataset, named DeepFakeFaceForensics (DF 3 ), whichcontains 6 state-of-the-art generation models and a variety of post-processingtechniques to approach the real-world scenarios. Experimental resultsdemonstrate the superiority of our method to the state-of-the-art methods onthe proposed DF 3 dataset and three other open-source datasets.

GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection