What is synthetic media?
Synthetic media is text, imagery, audio, video, or code that is generated or materially altered by artificial intelligence. It includes fully generated content and AI-assisted manipulation of real source material.
Coverage
Text, images, audio, video, and code in one product.
Access
A web checker for direct use and an API for integrations.
Interpretation
Probabilistic evidence that should be reviewed with source context.
Examples of synthetic media
Synthetic media appears in ordinary creative tools as well as fraud, impersonation, and misinformation workflows. These are common examples by content type.
- Text: an article, review, message, or essay written by an LLM.
- Image: a diffusion-generated scene or an AI-edited product photo.
- Audio: a cloned voice message or fully synthetic narration.
- Video: a face swap, lip-sync edit, or generated video clip.
- Code: source code produced by an LLM or coding assistant.
Generated, manipulated, and provenance-verified media
Not all synthetic media is created in the same way. A useful review separates how the content was made from whether its origin can be verified.
- Fully generated media is created primarily by a generative model.
- AI-manipulated media starts with real content and materially changes it.
- AI-assisted media combines human work with generated or edited elements.
- Provenance records can document origin and edits, but missing records do not prove that content is synthetic.
How to detect synthetic media
A reliable check combines several evidence layers because no single visual clue, metadata field, or model score works for every sample.
- Inspect available metadata and content provenance records.
- Use a detector built for the specific media type.
- Compare model confidence with localized forensic indicators.
- Verify the source, publication history, and surrounding context.
- Route uncertain or high-impact cases to human review.
Why synthetic media matters
Synthetic media can be useful for accessibility, entertainment, education, prototyping, and localization. Risk rises when generated or manipulated content is used without disclosure to deceive, impersonate, defraud, or evade platform policy.
- Disclosure and consent distinguish many legitimate uses from abuse.
- Source context matters as much as the media file itself.
- Detection results should support a documented decision, not replace one.
Common use cases
Newsroom and OSINT verification
Fraud and identity review
Trust and safety moderation
Methodology and limitations
How to read a result
Detection output is probabilistic evidence. A high score means the observed signals are consistent with synthetic or manipulated content under the current model and sample conditions. It does not prove authorship or intent.
When review is required
Short samples, heavy editing, compression, translation, re-recording, mixed human and AI content, and new generators can reduce confidence. Use human review before high-impact decisions.
Try ZeroTrue
Run a browser check, inspect the public API example, or create an account to keep results and generate an API key.
Frequently asked questions
What is an example of synthetic media?
A cloned voice message is one example. Other examples include an LLM-written article, a diffusion-generated image, a face-swap video, and code produced by a coding assistant.
Is synthetic media the same as a deepfake?
No. Deepfakes are one category of synthetic or manipulated media, usually involving a person's face, voice, or actions. Synthetic media also includes generated text, images, music, narration, and code.
Is synthetic media always harmful?
No. It has legitimate creative, educational, accessibility, and production uses. Harm depends on consent, disclosure, context, and how the content is used.
How can synthetic media be detected?
Combine a modality-specific detector with metadata, provenance, source verification, and human review. Each layer covers weaknesses in the others.
Can an AI detector prove that media is fake?
No. Detector scores are probabilistic evidence and can be affected by editing, compression, sample length, and new generators. They should not be treated as proof of authorship or intent.