Built for Investigators Handling Deceptive Media

Cyberette is an AI-native, multimodal media forensics platform that helps teams assess whether video, audio, or image may be manipulated or deceptive.

It is designed for investigation, intelligence, and security workflows where teams need fast screening, explainable indicators, and evidence-ready outputs to support human review and decision-making.

Manual review is slow, difficult to scale, and often hard to defend in high-stakes environments.
Cyberette helps teams:

• screen large volumes of media more efficiently
• identify suspicious or high-risk content earlier
• prioritize cases for analyst review
• generate structured forensic indicators and supporting evidence
• support decisions in investigations, intelligence, and incident response
• How does Cyberette detect deepfakes and AI threats?

Cyberette uses six complementary detection methods to verify every part of the
content:

Landmarking Analysis: Detects inconsistencies in facial features, micro-expressions, or unnatural face-to-background alignment.
Cross-Modality Checks: Aligns and compares audio and video streams to expose manipulation.
Behavioral Signal Analysis: Tracks subtle human signals like blinking, breathing or emotional cues to confirm liveness and authenticity.
Artifact-Based Detection: Examines files at the pixel and signal level to reveal synthetic traces such as irregular pixels.
Provenance Analysis: Verifies file authenticity and origin using metadata and provenance standards (C2PA) to expose irregularities or tampering.
Pattern-Based Detection: Monitors user and communication behavior to identify unusual patterns that may indicate manipulation or fraudulent activity.

Cyberette analyzes manipulated, synthetic, and deceptive media across video, audio, and image.

This includes:

• deepfake video
• voice cloning and synthetic audio
• AI-generated or manipulated images
• edited or tampered files
• deceptive multimodal content used in impersonation, disinformation, or social engineering scenarios

Cyberette is designed for organizations that need explainable forensic analysis in high-stakes environments.

Typical users include:

• investigation teams
• intelligence and OSINT teams
• forensic labs and analysts
• prosecutors and legal support teams
• protective security teams
• trust & safety and platform integrity teams
• organizations handling sensitive evidence or high-risk communications

Cyberette supports both operational screening and deeper forensic review, depending on the deployment model and workflow.

You can contact the Cyberette team to schedule a demo at info@cyberette.ai. To learn the latest news about Cyberette, you can follow us on LinkedIn

A deepfake is a piece of media (video, image, audio or even text) that has been digitally altered or generated by AI to convincingly mimic a real person or event. Most deepfakes are created using deep learning models such as generative adversarial networks (GANs), which learn a person’s face, voice or style from large datasets and then synthesize new, realistic content. Cyberette focuses specifically on detecting these AI manipulations in real time, across video, audio, image, so teams can verify
what they see and hear before making important decisions.

Deepfakes can be detected through a combination of AI-based analysis, media forensics, and human review. Cyberette analyzes video, audio, and image content for
subtle signals that may indicate manipulation or synthetic generation, including visual artifacts, behavioral cues, cross-modal inconsistencies, and metadata or provenance irregularities. The platform helps teams screen streaming or uploaded media and
identify suspicious content for further review in investigation, intelligence, security, and trust workflows.

Deepfakes themselves are not always illegal, there are legitimate uses in accessibility and entertainment, but many uses of deepfakes are: fraud, non-consensual sexual content, election interference, identity theft or defamation may breach criminal, civil or data-protection laws. Regions are rapidly introducing specific rules: for example, the EU AI Act will require
clear labeling of AI-generated content.

Cyberette does not provide legal advice, but our detection tools help organizations in Europe, the US and across the world comply with emerging transparency and content authenticity requirements.

To be completely safe from deepfakes you need both behavior and technology:

• Treat urgent, emotional messages (especially about money, credentials or sensitive data) with extra suspicion, even if the face or voice looks familiar.
• Verify high-risk requests using a second channel (for example, call back on a known number or confirm via a secure app).
• Ask a personal question that only you and the said person know the answer correctly.
• Watch out for inconsistent noises and background images (for example: hearing loud car noise when the said person is at a café)
• Train employees and users to recognize deepfake warning signs and have a clear internal process to report suspicious content.
• Deploy dedicated solutions like Cyberette across high-risk channels, video calls, onboarding, payments, media publishing, so suspicious content is automatically scanned, scored and escalated.

Studies show humans correctly identify high-quality deepfake videos only about a quarter of the time, making Cyberette a must have before making any important decisions

Deepfakes can sometimes be tracked, but it depends on where they’re posted and what data is available. Investigators may use:

• Watermarks and provenance standards (such as C2PA-style content credentials)
• File metadata, hashes and unique compression patterns
• Platform logs, account histories and network information
• Specialized forensic and law-enforcement tools for large-scale monitoring

Cyberette’s role in this is to reliably flag and score suspect content so platforms, risk teams or investigators know what to look at first, and can combine the results with their own logs and legal processes. As more regions adopt watermarking and transparency rules for AI-generated content, detection and attribution will become easier over time.

A deepfake attack occurs every 5 minutes, While 1 in 4 leaders unfamiliar with deepfakes and 24–25% of humans correctly identify high-quality deepfake videos, deepfake files surged from 500K in 2023 to 8M in two years. Most people are worried about deepfakes but
not confident they can recognize them