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 detection and localization of highly realistic deepfake audio-visualcontent are challenging even for the most advanced state-of-the-art methods.While most of the research efforts in this domain are focused on detectinghigh-quality deepfake images and videos, only a few works address the problemof the localization of small segments of audio-visual manipulations embedded inreal videos. In this research, we emulate the process of such contentgeneration and propose the AV-Deepfake1M dataset. The dataset containscontent-driven (i) video manipulations, (ii) audio manipulations, and (iii)audio-visual manipulations for more than 2K subjects resulting in a total ofmore than 1M videos. The paper provides a thorough description of the proposeddata generation pipeline accompanied by a rigorous analysis of the quality ofthe generated data. The comprehensive benchmark of the proposed datasetutilizing state-of-the-art deepfake detection and localization methodsindicates a significant drop in performance compared to previous datasets. Theproposed dataset will play a vital role in building the next-generationdeepfake localization methods. The dataset and associated code are available athttps://github.com/ControlNet/AV-Deepfake1M .

AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset