Our research sits at the intersection of computer vision, generative models, and human-centric AI. Every protection and detection method we ship has been replicated, validated, and stress-tested — not just benchmarked internally. Published science, satisfying the Daubert standard for scientific evidence in courts.
Detecting synthetic media through "real" signals that generative models are not yet trained to replicate.
Protecting likeness, ownership, and privacy from generative AI. Unlike hours of perturbation, imperceptible generation.
Tracing synthetic media backward to its source, reference, and process. Completing the life-cycle of content, bit by bit.
Extending protection & detection beyond faces — satellite imagery deepfakes, audio-visual protection, manipulated scenes, and cross-modal algorithms.
AI for humans, not the other way. Every prediction with evidence and explainability. Every generation with control and options. Augmenting human agency — not replacing them.
Embedding safety and accountability directly into generation — not as a filter. Watermarking, synthetic manifolds, and intentional refusal in the creative pipeline.