An AI-powered investigation platform for discovering and verifying suspected deepfake videos across supported online platforms, with findings organized into investigation cases.
Project overview
Deepfake Finder automates the process of finding possible deepfake videos of a target person. Instead of manually searching multiple websites, the platform continuously scans supported platforms, collects relevant media, verifies whether the target person actually appears, and performs AI-based deepfake analysis.
Web intelligence, computer vision, and AI verification come together in one investigation workflow. Findings are presented in an organized dashboard and stored as cases for future review.
AI assists investigators, but final decisions should always include human review.
Multi-platform
Searches supported public sources in one investigation
AI-assisted
Identity verification before detailed deepfake analysis
Case-based
Historical results retained with every investigation
Continuous
Scanning can repeat for newly published content
How it works
01
Investigator enters the target name
02
A case is created for the investigation
03
Web, social media and dark web sources scanned
04
Relevant videos and thumbnails collected
05
AI verifies whether the target person appears
06
Collected media receives AI-based analysis
07
Previously processed content is automatically skipped
08
Findings are organized as investigation results
The scanning pipeline can repeatedly search for newly published content while preserving historical case results.
Project exclusiveness
One platform searches multiple public sources instead of requiring investigators to search manually.
Multi-platform search
Content is filtered before detailed analysis, reducing unnecessary processing.
Identity verification first
Every investigation is stored as a case with historical results.
Historical case records
The scanning pipeline can repeatedly search for newly published content.
Repeatable scanning
Previously processed content is automatically skipped to save computation.
Duplicate detection
Designed for digital investigation, cyber intelligence, and media verification workflows.
Investigation-focused design
Market comparison
Multi-platform search
AI identity verification
Deepfake analysis
Investigation cases
Continuous monitoring
Duplicate detection
Dashboard visualization
The architecture
Nothing is analysed until the target is confirmed to appear in it
Investigation case
A case is opened, and everything below is attached to it
Gate 01 — identity verification
Media the target does not appear in never reaches deepfake analysis.
Gate 02 — deepfake detection
AI assists investigators at both gates. Final decisions always include human review.
Potential use cases
Monitor the internet for AI-generated videos that impersonate executives, government officials, or threat actors to help identify misinformation campaigns, social engineering attempts, and malicious influence operations early.
Verify whether a video is genuine or AI-generated while collecting evidence from multiple online sources and organizing findings into investigation cases.
Help news organizations and fact-checkers verify suspicious viral videos before publication, reducing the spread of misinformation.
Monitor for deepfake videos that misuse executives, spokespersons, or brand identity so organizations can respond to scams, fake advertisements, or reputation-damaging content.
Locate manipulated videos, gather publicly available evidence, and track harmful synthetic media across multiple platforms during cybercrime and fraud investigations.
Automate collection of publicly available media from websites and social platforms, giving analysts centralized results for further analysis.
Support public figures, journalists, influencers, celebrities, and organizations in monitoring for fake videos made using their identity.
Deployment strategy
Agree which supported platforms are in scope, who the targets are, and what the team needs out of an investigation — evidence for a case file, an early warning on a campaign, or ongoing monitoring of a public figure.
Each target is enrolled with reference media so identity verification has something to match against. This is what lets the platform discard collected media the target does not appear in before any analysis runs.
The first pass searches the supported sources, collects relevant media, verifies the target, analyses what survives, and writes the findings into the investigation case. This establishes the baseline everything later is measured against.
Investigators work from the dashboard: findings organized per case, historical results retained, and every item traceable back to where it was collected. AI assists at both gates, but flagged media is reviewed by a person before it is acted on.
The case stays open and the scan repeats on a schedule that suits the risk. Previously processed content is skipped automatically, so each pass spends its effort on newly published material rather than re-analysing what has already been seen.
Why this project matters
Deepfake Finder combines automated intelligence gathering, AI-assisted identity verification, and deepfake analysis in one unified investigation platform.
Frequently asked questions
It automates the discovery and verification of suspected deepfake content from multiple online sources.
No. It is designed for detection and investigation purposes.
Yes. AI assists investigators, but final decisions should always include human review.
Yes. The scanning pipeline can repeatedly search for newly published results.
Yes. Investigations are organized into cases with historical records.
Cybersecurity analysts, journalists, researchers, investigators, and digital forensics teams.
The rest of the line
See how Deepfake Finder searches supported sources, verifies possible matches, analyses media, and organizes findings into cases.
