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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

With the rise of cameras and smart sensors, humanity generates an exponentialamount of data. This valuable information, including underrepresented caseslike AI in medical settings, can fuel new deep-learning tools. However, datascientists must prioritize ensuring privacy for individuals in these untappeddatasets, especially for images or videos with faces, which are prime targetsfor identification methods. Proposed solutions to de-identify such images oftencompromise non-identifying facial attributes relevant to downstream tasks. Inthis paper, we introduce Disguise, a novel algorithm that seamlesslyde-identifies facial images while ensuring the usability of the modified data.Unlike previous approaches, our solution is firmly grounded in the domains ofdifferential privacy and ensemble-learning research. Our method involvesextracting and substituting depicted identities with synthetic ones, generatedusing variational mechanisms to maximize obfuscation and non-invertibility.Additionally, we leverage supervision from a mixture-of-experts to disentangleand preserve other utility attributes. We extensively evaluate our method usingmultiple datasets, demonstrating a higher de-identification rate and superiorconsistency compared to prior approaches in various downstream tasks.

Disguise without Disruption: Utility-Preserving Face De-Identification