As surveillance technology continues to expand—particularly facial recognition—protecting the identity of individuals in photos and videos has become critical for journalists, researchers, activists, and whistleblowers. Anonymous Camera was built to meet that need, using state-of-the-art machine learning to anonymize sensitive footage at the point of capture.

The app performs real-time, on-device anonymization of faces and bodies using advanced computer vision models. Users can choose between blur, pixelation, noise, or solid masks to anonymize identities—whether partially (just the face) or fully (entire body). Anonymization works reliably across angles, lighting conditions, and motion, making it suitable for dynamic field recording.

Beyond visuals, Anonymous Camera also anonymizes audio and metadata. Users can distort voices on the audio track and remove all identifying metadata—including time, location, and device information—from photos and videos. Interviews can be partially anonymized using a split-screen mode, allowing for flexible framing while still protecting sources.

All processing and storage happens locally. No data is sent to the cloud, and the app can function entirely offline—even in airplane mode—ensuring user control from start to finish.

Since launch, Anonymous Camera has seen over 100,000 downloads, with active usage in regions affected by conflict and political instability. It was recognized with an iF Design Award in 2021 and featured by The Verge, Wired, and 9to5Mac for its role in advancing privacy-first tools for high-risk environments.