Powered by advanced media detection models

Is this media Real or AI?

Review image, video, and audio content with clear authenticity signals, readable confidence scores, and a workflow designed for actual verification, not guesswork.

Deepfake image, audio, and video detection included

Swipe left or right on touch devices to move between image, video, and audio demo results.

Image Analysis
sample_photo.jpg
Likely AI-Generated
87%

Synthetic texture patterns and generator-style artifacts detected. High confidence match with known generators.

AI Generated
87%
Authentic
13%

Detected generators

Midjourney
72%

Diffusion artifacts + lighting patterns

DALL·E
18%

Text rendering inconsistencies

Stable Diffusion
6%
Flux
3%
Other
1%

Live app

Auth, database, and scan flow already running in production

3 media

Built around image, video, and audio authenticity review

Fast loop

Create a scan, run analysis, and review results in one place

Credits

Wallet-driven usage model ready for product growth

Built for creators, analysts, and trust teams who need clearer authenticity signals.

Built for clarity, not clutter

Everything realvs needs to feel like a serious authenticity product, not a thin demo skin.

Unified scan flow

Start from one clean surface and move from media input to result review without switching tools.

Evidence-led output

Read confidence, verdict, and scan context in a way that supports actual human review.

Live product foundation

Realvs already runs with auth, database wiring, wallet logic, and processor-backed scans.

Coming soon

Scan history

A stronger review workspace for revisiting past decisions and scan activity.

Team workflows

Collaboration-friendly review paths for moderation, media ops, and trust teams.

Shareable reports

Cleaner exports and summaries for clients, evidence, and internal review.

Developer API

A productized interface for embedding authenticity checks into external systems.

Broader ingestion

Expanded support for more input paths beyond the current direct media workflow.

How it works

Three steps to move from media input to a more readable authenticity decision.

01

Paste a direct media source

Start with the current realvs input flow and create a scan around the content you want to verify.

02

Run analysis through the scan pipeline

The app processes the request, executes the configured detector layer, and records the result state.

03

Review a clearer authenticity signal

Read verdict, confidence, and scan output in a format built for decision-making, not guesswork.

Important note

Authenticity detection should be treated as a confidence signal, not absolute proof. Realvs is designed to support review and triage, with humans still making the final call.

Deep scan analysis

Realvs presents authenticity analysis as a layered review surface built from multiple detection signals.

Visual artifacts

Surface-level inconsistencies, rendering defects, and synthetic image traces.

Temporal patterns

Frame-to-frame instability, motion irregularities, and sequence drift.

Audio markers

Voice synthesis clues, spectral anomalies, and cloned speech indicators.

Metadata clues

Container, codec, and file-level evidence that supports deeper review.

Compression traces

Re-encoding patterns and artifact signatures left during media generation.

Face consistency

Facial geometry, blending issues, and deepfake-style mismatch signals.

Generator fingerprints

Patterns associated with known AI generation and editing pipelines.

Confidence fusion

Multiple signals combined into one readable authenticity decision.

Risk scoring

A verdict layer built to help teams prioritize suspicious content faster.

Analyst review

Readable output designed for human verification, not black-box guessing.