Transparency
What we disclose, why we disclose it, and where to find the details.
Our transparency commitment
Transparency is foundational to trust in a forensic analysis platform. If you can't understand how our assessments are produced, you can't meaningfully evaluate them. We publish information about our methods, accuracy, limitations, and practices so that users can make informed judgments.
What We Publish
Transparency disclosures
Methodology
Complete documentation of our scoring methodology including module weights, confidence calibration, evidence fusion process, and verdict thresholds.
Read methodology →Detection limitations
Honest disclosure of scenarios where our analysis is less reliable: novel generators, adversarial attacks, heavy compression, short clips, and more.
Read limitations →Validation results
Internal validation study with accuracy metrics, per-module performance, false positive rates, and known failure modes.
Read validation study →Ethics framework
Our principles for responsible AI detection: honest communication, false positive awareness, non-weaponization, privacy protection, and equitable access.
Read ethics →Module documentation
Detailed documentation of all 15 forensic modules including what they analyze, how they work, and what they can and cannot detect.
Read module docs →Research publications
Educational content about deepfake detection, video forensics, and media authenticity. We publish what we know to help the broader community.
Read research →Data handling
Video storage: Uploaded videos are processed and stored securely in your account. We do not share your videos with third parties.
Analysis results: Forensic reports are associated with your account and visible only to you. You can delete them at any time.
Training data: We do not use your uploaded videos to train our models without explicit, opt-in consent.
Analytics: We collect anonymized usage analytics (page views, feature usage) to improve the platform. No video content is included in analytics.