The platform was built with:
- Secure AI model pipelines
- Scalable cloud-native infrastructure
- Encrypted media processing workflows
- High-volume media analysis architecture
- Enterprise-focused AI deployment standards
FakeXpose was created to address a growing challenge in the AI era: the rapid rise of fake videos, cloned voices, and manipulated digital media targeting public figures and organizations.
Backed by the University of Michigan, the project focused on building a custom ML-based deepfake detection platform trained on media patterns from influential public figures to help identify manipulated audio and video content more accurately.
Origami Studios developed the AI platform with explainability features that not only detected fake media but also explained why specific content was flagged through confidence scoring and forensic-level reasoning.
AI-generated media manipulation was becoming increasingly difficult to identify using traditional verification methods.
The project required a system capable of:
Most available deepfake detection tools lacked transparency, making it difficult for users to trust or validate detection results in high-risk scenarios involving public figures and sensitive media content.
The platform also needed scalable AI processing pipelines capable of analyzing both video and audio inputs efficiently.
Origami Studios developed FakeXpose as a custom AI-powered deepfake detection platform using machine learning, computer vision, and audio analysis models.
The platform was trained on media datasets associated with highly influential public figures to improve pattern recognition across manipulated content scenarios.
The system analyzed:
A key part of the platform was its explainable AI layer, which generated:
This helped users understand why media was flagged rather than relying on black-box AI predictions.
Deepfake detection accuracy
Across every test dataset fake videos, cloned voices, and manipulated images flagged with forensic-level precision.
Computer Vision + Deep Learning
Average media verification time
Audio and video analysed end-to-end in seconds no manual forensic review needed for routine verification cases.
Real-time media processing pipeline
Unified multimodal detection
A single platform detecting fakes across video, audio, and image replacing three separate manual workflows with one AI system.
Video + Audio + Image models
Explainable AI verification outputs
Every detection decision comes with confidence scores, forensic reasoning, and media authenticity indicators so users understand exactly why a piece of content was flagged, not just that it was
Biggest trust & transparency impact
Cloud-native high-volume architecture
Built to process high volumes of suspicious media across pipelines organisations can scale verification without adding analysts or slowing detection speed.
Enterprise-ready AI infrastructure