Protect every identity in public.
Towards realizing our mission, we are providing the most advanced solutions to protect personally identifiable information in camera data. Anonymizing images and videos is crucial to comply with increasing privacy regulations like GDPR, CCPA, APPI and CSL. At the same time, data quantity and quality are the backbone of new innovations powered by analytics and machine learning. With our solutions, there is no trade-off and the future of sustainable, consumer-centered innovation comes one step closer.
Face Anonymization
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
brighter AI provides license plate redaction and artificial overlays that appear natural. Maintaining a natural appearance is crucial in use cases that involve machine learning, for example for the development of autonomous vehicles or ADAS functions. In these use cases, 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
