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Protect every identity in public.
Identity Protection Suite
Anonymizing publicly-recorded images is important to comply with increasing data privacy regulations like GDPR and CCPA. brighter AI’s Identity Protection Suite provides the most advanced solutions to protect personally identifiable information in camera data. The software is container-based and provides a RESTful API. As it supports all standard image, video and streaming formats, the Identity Protection Suite can be used for a great variety of use cases.
Face Anonymization
Faces are our most sensitive identifiers in public. With increasing capabilities of facial recognition technology, large datasets from CCTV, automotive or smartphone cameras carry substantial risk. brighter AI provides Precision Blur, the most advanced face redaction solution, and Deep Natural Anonymization. With this solution, synthetic face overlays protect individuals from face recognition. At the same time, the data is still usable for analytics and machine learning.
License Plate Anonymization
In mobility datasets, license plates are the most present personal identifiers. brighter AI provides license plate redaction and artificial overlays that appear natural. Maintaining a natural appearance is particularly important in use cases that involve machine learning, for example for the development of autonomous vehicles or ADAS functions. Deep Natural Anonymization has no adverse impact on neural networks.
Benefits
Integrate seamlessly
(on-premise, cloud, edge)
Accelerate data
collection
Empower data
exchange
Protect identities against
automatic recognition
Comply with regulations
(e.g. GDPR, CCPA)
Enable analytics & AI
(natural anonymization)
Why Deep Natural Anonymization?
By replacing faces and license plates with natural-looking overlays, Deep Natural Anonymization is the only privacy solution for videos compatible with analytics and machine learning.
Award-winning technology
Compatibility with machine learning
“Brighter AI’s natural anonymization solution has no adverse impact on the performance of our machine learning models.”
AV Development Lead, Automotive Tier 1
Training with the Original Data vs. Deep Natural Anonymization Technology (DNAT); Cityscapes Validation Dataset
