Verdict
Real
Signature matches real-image distribution. No flags.
Media authentication grounded in forensic science for high-stakes decisions
Unsurface applies peer-reviewed forensic methods to detect, localize, and explain synthetic and manipulated visual media — for newsrooms, defense, intelligence, and law enforcement teams whose work depends on knowing what's real. Our core technology, Forensic Self-Descriptions, was peer-reviewed at CVPR 2025.
EV·01 
Verdict
SYNTHETIC
Signature deviates strongly from the real-image distribution.
Synthetic Real
Probability AI-generated 100%
Attribution
- Nano Banana v1/Pro/2 71.6%
- Grok 7.4%
- Real photograph 7.0%
+ 11 further families below reporting threshold.
Detect
Identify synthetic images and video produced by diffusion models, GANs, and other generative systems — including generators Unsurface has never encountered during training. Zero-shot detection built on forensic traces, not on recognizing specific models.
Attribute
Beyond real-or-synthetic: cluster imagery by the family of generator that likely produced it, even within a single case, and even when the source is unknown. Open-set attribution that scales with the generative ecosystem as new models appear.
Explain
Every analysis produces interpretable signals your team can inspect, discuss, and document. Not a black-box verdict — a forensic report designed for expert review, defensible in editorial debate and investigative work.
ROADMAP
On the roadmap
Unsurface is actively expanding what forensic analysis can do — adding capabilities our founding partners have identified as the highest-leverage additions to their verification workflows.
Region-level localization
Identify exactly where an image or video frame has been altered, not just whether it has. Turning "this looks off" into "this is what's off, and here."
Agentic investigation for journalists
In-house tooling that works alongside your team to trace the provenance of visual media and cross-reference public sources, accelerating open-source investigations that currently take days.
Face-swap and identity manipulation detection · detection of general video editing and manipulation · expanded attribution to video-native generative systems.
RESEARCH
Built on peer-reviewed forensic science
Unsurface's core technology is Forensic Self-Descriptions (FSD), peer-reviewed at CVPR 2025 — the top venue in computer vision.
FSD learns the subtle, pixel-level traces that every image creation process leaves behind, without needing prior examples of any specific generator. It was the first method to unify zero-shot detection, open-set source attribution, and forensic clustering of AI-generated imagery in a single framework.
Request access
We work with a limited number of founding partners to shape Unsurface for high-stakes verification workflows. If your team makes decisions that depend on the authenticity of visual media, we'd like to hear about your use case.