Witness VS Nullstack

Witness's answer

• Individual users checking suspicious images and dating profiles
  
• Journalists verifying source media before publishing
  
• Parents installing the Chrome extension on family devices
  

Witness's answer

Two audiences. Consumers who need to verify something they saw online: a suspicious dating profile, a viral image, a video that looks off. And enterprise teams who screen media at volume: insurance investigators checking claim photos, e-commerce fraud teams reviewing refund evidence, legal professionals authenticating digital exhibits, and trust & safety teams moderating user content.

Witness's answer

Witness started because deepfake detection tools either didn't work well enough for regular people or were locked behind enterprise sales calls. We wanted to build something that gives a clear, honest answer to a simple question: is this real, or was it made by AI? The name comes from the idea that when you can tell the difference, you become a witness. You see clearly and you can say what you saw.

Witness's answer

Most detection tools run one model and give you one number. Witness runs multiple checks on every scan and combines the signals, which means fewer wrong answers on the cases that matter. It also reads C2PA content credentials, a cryptographic proof of how media was created. When provenance data confirms the origin, you get a near-certain answer without relying on statistical detection alone.

Witness's answer

Three reasons. First, Witness checks images and videos, including face swap detection in video, which most competitors do not offer. Second, the Chrome extension works on sites where other tools fail (Twitter/X, Instagram, Reddit) because of how it fetches images. Third, Witness is transparent about what it can and cannot detect, rather than claiming a single accuracy number with no methodology behind it.

Witness's answer

Deep learning models trained on large-scale datasets covering 20+ AI generators. C2PA content credential verification for cryptographic provenance. Next.js frontend, Python backend on GPU infrastructure. The Chrome extension uses a custom media fetch pipeline built for compatibility with major social platforms.