> For the complete documentation index, see [llms.txt](https://agpt.co/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://agpt.co/docs/integrations/block-integrations/deepfake.md).

# Nvidia Deepfake

Blocks for detecting deepfakes and synthetic image manipulation using Nvidia AI.

## Nvidia Deepfake Detect

### What it is

Detects potential deepfakes in images using Nvidia's AI API

### How it works

This block analyzes images using Nvidia's AI-powered deepfake detection model. It returns a probability score (0-1) indicating the likelihood that an image has been synthetically manipulated.

Set return\_image to true to receive a processed image with detection markings highlighting areas of concern.

### Inputs

| Input         | Description                                         | Type       | Required |
| ------------- | --------------------------------------------------- | ---------- | -------- |
| image\_base64 | Image to analyze for deepfakes                      | str (file) | Yes      |
| return\_image | Whether to return the processed image with markings | bool       | No       |

### Outputs

| Output       | Description                                                     | Type       |
| ------------ | --------------------------------------------------------------- | ---------- |
| error        | Error message if the operation failed                           | str        |
| status       | Detection status (SUCCESS, ERROR, CONTENT\_FILTERED)            | str        |
| image        | Processed image with detection markings (if return\_image=True) | str (file) |
| is\_deepfake | Probability that the image is a deepfake (0-1)                  | float      |

### Possible use case

**Content Verification**: Verify authenticity of user-uploaded profile photos or identity documents.

**Media Integrity**: Screen submitted images for signs of AI manipulation.

**Trust & Safety**: Detect potentially misleading synthetic content in social or news platforms.

***


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://agpt.co/docs/integrations/block-integrations/deepfake.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
