The only certified value preserving video redaction software enabling GDPR compliance
Protect every identity in public.
We develop game-changing image and video anonymization software to protect identities. Anonymization is crucial to comply with privacy regulations like GDPR, CCPA, APPI and CSL. At the same time, data quality is the backbone of AI innovation and machine learning. With our data protection software, there is no trade-off between privacy and video analytics.
Face Anonymization
With increasing capabilities of facial recognition technology, public video data collection comes with great risks. brighter AI’s Precision Blur is the most accurate face redaction solution in the world. Deep Natural Anonymization is a unique privacy solution based on generative AI. It creates synthetic face overlays to protect individuals from recognition, while keeping data quality for machine learning.
License Plate Anonymization
With Precision Blur, brighter AI offers advanced video redaction for license plates worldwide. Deep Natural Anonymization is image and video anonymization software that replaces license plates with replicas. Therefore, it has no negative impact on neural networks and can be used for autonomous driving.
Benefits
Strong data protection
made in Germany
In line with regulations
(GDPR, CCPA, CSL)
Seamless integration
(cloud API, on-premise, edge)
Accelerating data
collection
Empowering data
exchange
Enabling analytics & AI
(natural anonymization)
Why Deep Natural Anonymization?
Deep Natural Anonymization protects personal information in images with artificial replacements. Therefore, it is the only video redaction solution compatible with analytics and machine learning.
Industries
Automotive
for training and serial production
Healthcare
for sensitive medical image and video data
Public Sector
for insights and security
Research
for processing and collaboration
Award-winning technology
Compatibility with machine learning
“brighter AI’s solution was easily integrated and the natural anonymization was what we needed for improvement of lane and sign detection validation strategy.”
Václav Schybal, System Validation Platform Manager, Valeo
Training with the Original Data vs. Deep Natural Anonymization Technology (DNAT); Cityscapes Validation Dataset