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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Recently, the rapid development of video editing software has made video forgery applicable. Researchers have proposed methods to detect forged video frames. These methods utilize codec properties, motion artifacts, noise effect and frame similarity to detect forgery. Execution time and low detection accuracy are the two main drawbacks of forgery detection methods reported in the literature. In this study, a new frame duplication detection method using Local Difference Binary (LDB) is proposed to extract features from the frames. Distance between similar frames that have similar feature vectors are is used by the method to estimate Distance of Forgery and to determine the exact location of duplicated frames. PSNR between similar frames are is then used to group them into three classes, and rule-based mechanism reports forged frames according to the membership to classes. Experimental results indicate that the proposed method has lower execution time with higher accuracy than similar works.

Video Forgery Detection Method based on Local Difference Binary