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

Hyper-realistic face image generation and manipulation have givenrise tonumerous unethical social issues, e.g., invasion of privacy,threat of security,and malicious political maneuvering, which re-sulted in the development ofrecent deepfake detection methods with the rising demands of deepfakeforensics. Proposed deepfake detection methods to date have shown remarkabledetection performance and robustness. However, none of the suggested deepfakedetection methods assessed the performance of deepfakes with the facemaskduring the pandemic crisis after the outbreak of theCovid-19. In this paper, wethoroughly evaluate the performance of state-of-the-art deepfake detectionmodels on the deepfakes with the facemask. Also, we propose two approaches toenhance the masked deepfakes detection: face-patch and face-crop. Theexperimental evaluations on both methods are assessed through the base-linedeepfake detection models on the various deepfake datasets. Our extensiveexperiments show that, among the two methods, face-crop performs better thanthe face-patch, and could be a train method for deepfake detection models todetect fake faces with facemask in real world.

Deepfake Detection for Facial Images with Facemasks