Detesia provides an explainable deepfake detection tool for images and videos, offering precision in uncovering synthetic media. The solution utilizes a multi-detector framework with specialized AI models and non-AI forensic techniques for comprehensive analysis. It delivers insights through reverse image searches, metadata analysis, and digital watermark verification to establish media authenticity.
Funding
Funding not disclosed
Founders
Product
Problem
Synthetic media such as deepfake images and videos are increasingly used to spread misinformation, commit fraud, and manipulate public perception. Existing detection tools often operate as opaque black boxes, providing little insight into why content is flagged and lacking comprehensive forensic analysis. This limits trust and hampers timely decision‑making for organizations that must verify digital authenticity.
Solution
Detesia delivers an explainable deepfake detection platform that combines a suite of specialized AI models with traditional forensic techniques. Each model is optimized for a specific manipulation type—face swaps, lip‑sync edits, diffusion‑generated content—allowing precise identification across diverse deepfake families. The system generates visual explanations and decision rationales, so users can understand the evidence behind each classification. In addition to AI analysis, Detesia extracts metadata, validates digital watermarks, and performs reverse‑image searches to trace media provenance. The service is available via a cloud‑based API for seamless workflow integration or as an on‑premise deployment for environments with strict data‑privacy requirements. Real‑time inference and batch processing are supported, and the platform complies with GDPR and other data‑protection standards.
Target Audience
Primary customers include law‑enforcement agencies, media and publishing organizations, online content platforms, and financial institutions that require reliable verification of image and video authenticity. The solution also serves security teams and compliance officers responsible for mitigating deepfake‑related risks.
Features
- Multi‑detector framework employing CNN and transformer models tuned for face‑swap, lip‑sync, and diffusion‑based deepfakes
- Explainable AI output with heat‑map visualizations and textual rationale for every detection decision
- Forensic modules that extract EXIF metadata, verify digital watermarks, and conduct reverse‑image searches to locate prior appearances of the media
- GPU‑optimized inference pipeline delivering sub‑second real‑time detection or high‑throughput batch processing
- RESTful API and SDKs (Python, JavaScript) for easy integration into content management systems, social platforms, and security tools
- Optional on‑premise deployment package enabling full data residency and isolation for sensitive use cases
- End‑to‑end encryption and GDPR‑compliant data handling throughout upload, analysis, and result storage