Do you see it?
We do.
Detect manipulated audio, video, and images with explainable forensic AI.
Built for courts, investigators, insurers, and enterprise security teams.
Developed from University of Michigan research · Trusted by partners in North America and Asia.
A Timeless Problem. A Modern Threat.
Fakes and forgeries have existed since time immemorial. With generative and agentic AI, the technology to create fakes has changed, therefore your institution or company needs up-to-date detection and verification across your most critical operations:
Legal & Courts
Defensible digital evidence
Insurance & Claims
Synthetic media in claims investigations
Fraud & Financial Crime
Deepfake fraud and identity manipulation
Cybersecurity & Insider Risk
Impersonation and social engineering attacks
Detection You Can Defend
Spellbreaker combines multiple forensic methods to support reliable media verification.
Wide detection range
Detects basic, partial, shallow and complex fakes with high precision
Contextual analysis
Supports ambiguous or complex scenarios
Explainable reporting
Transparent, defensible findings
Robust to degraded media
Detect even low-quality or compressed inputs
Zero-day detection
Identify emerging manipulation patterns
Bias-aware evaluation
Ensuring transparent AI-driven decisions for greater trust
How Spellbreaker Works
Ingest
Securely submit video, audio, and image evidence through the web interface or API.
Spellbreaker prepares the media for inspection while preserving evidentiary integrity.
Detect
Analyze media using multiple detection models that identify spatial, signal, and cross-modal inconsistencies.
Spellbreaker identifies manipulation patterns that generic detection tools often miss.
Explain
Generate a transparent analysis report that highlights suspicious regions and documents how conclusions were reached.
Investigators can review the findings and use them in legal, investigative, or security workflows.
Example Analysis Output
Spellbreaker analyzes digital media and produces a structured report explaining where and how manipulation was detected.
Evidence
Submitted media for inspection.
Analysis
AI analysis highlights regions where visual or signal characteristics deviate from authentic media.
Findings
Manipulation Detected
Confidence: High
Manipulation indicators:
- Pixel frequency anomalies
- Lighting inconsistency
- Edge artifact patterns
Enterprise Ready from Day One
Spellbreaker supports controlled evaluation and structured deployment.
From controlled pilot to full organizational deployment, each stage provides measurable validation and reduces implementation risk.
API integration allows Spellbreaker to connect with existing investigation, claims, and security systems.
Built on Trusted Research. Designed for Real-World Use.
Spellbreaker combines legal and investigative expertise with AI research from the University of Michigan.
Our focus is practical: transparent analysis and defensible reporting.