4 releases

0.1.3 Jun 13, 2025
0.1.2 May 30, 2025
0.1.1 May 29, 2025
0.1.0 May 29, 2025

#3 in Multimedia

Download history 368/week @ 2025-05-28 133/week @ 2025-06-11

501 downloads per month

Apache-2.0

86KB
2K SLoC

Reality Defender Rust SDK

codecov

The Reality Defender Rust SDK provides a simple and efficient way to integrate deepfake detection capabilities into your Rust applications.

Features

  • Asynchronous API built on Tokio
  • Type-safe interfaces with Serde for serialization
  • Secure file uploads using presigned URLs
  • Comprehensive error handling
  • High test coverage

Installation

Add the SDK to your Cargo.toml:

[dependencies]
realitydefender = "0.1.2"

Usage

Basic Example

use realitydefender::{Client, Config, UploadOptions};
use std::env;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize the client with your API key
    let client = Client::new(Config {
        api_key: env::var("REALITY_DEFENDER_API_KEY")?,
        ..Default::default()
    })?;

    // Upload a file for analysis
    let upload_result = client.upload(UploadOptions {
        file_path: "./image.jpg".to_string(),
        metadata: None,
    }).await?;

    println!("Request ID: {}", upload_result.request_id);

    // Get the analysis result with waiting for completion
    let result = client.get_result(
        &upload_result.request_id,
        Some(realitydefender::GetResultOptions {
            wait: Some(true),
            timeout_seconds: Some(60),
        }),
    ).await?;
    
    println!("Status: {}", result.status);
    if let Some(score) = result.score {
        println!("Score: {:.4} ({:.1}%)", score, score * 100.0);
    }

    // Access model-specific results
    for model in result.models {
        if model.status != "NOT_APPLICABLE" {
            println!(
                "Model: {}, Status: {}, Score: {:.4}", 
                model.name, 
                model.status, 
                model.score.unwrap_or(0.0)
            );
        }
    }

    Ok(())
}

Processing Multiple Files

use realitydefender::{Client, Config, BatchOptions};
use std::env;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize the client
    let client = Client::new(Config {
        api_key: env::var("REALITY_DEFENDER_API_KEY")?,
        ..Default::default()
    })?;

    // Process multiple files concurrently
    let results = client.process_batch(
        vec!["./files/image1.jpg", "./files/image2.jpg", "./files/video.mp4"],
        BatchOptions {
            max_concurrency: Some(3),
            wait: Some(true),
            timeout_seconds: Some(120),
        }
    ).await?;

    // Print results
    for (idx, result) in results.iter().enumerate() {
        println!("File {}: Status: {}", idx + 1, result.status);
        if let Some(score) = result.score {
            println!("  Score: {:.4} ({:.1}%)", score, score * 100.0);
        }
    }

    Ok(())
}

Simplified Detection

use realitydefender::{Client, Config};
use std::env;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize the client
    let client = Client::new(Config {
        api_key: env::var("REALITY_DEFENDER_API_KEY")?,
        ..Default::default()
    })?;

    // Detect a file with a single call
    let result = client.detect_file("./files/image.jpg").await?;
    
    println!("Status: {}", result.status);
    if let Some(score) = result.score {
        println!("Score: {:.4} ({:.1}%)", score, score * 100.0);
    }

    Ok(())
}

Running the Examples

The SDK comes with several examples that demonstrate how to use its features. To run these examples, you need to set your API key as an environment variable:

export REALITY_DEFENDER_API_KEY=your_api_key_here

Then, you can run the examples using Cargo:

# Run the basic example
cargo run --example basic

# Run the batch processing example
cargo run --example batch_processing

Required Test Files

To run the examples successfully, you'll need to add your own image and video files to the files directory:

  1. Create an files directory in the root of the project (if it doesn't already exist):

    mkdir -p files
    
  2. Add the following files to this directory:

    • image1.jpg - Any sample image for testing image analysis
    • image2.jpg - Another sample image
    • test_image.jpg - A third test image
    • video1.mp4 - A sample video file for testing video analysis

You can use any JPG files and MP4 videos for testing purposes. The examples are configured to use these specific filenames from the files directory:

// Using the sample files in your code
let result = client.detect_file("./files/image1.jpg").await?;

// For batch processing
let results = client.process_batch(
    vec!["./files/image1.jpg", "./files/image2.jpg", "./files/video1.mp4"],
    BatchOptions::default()
).await?;

Note: If you prefer to use different filenames or paths, make sure to update the example code accordingly.

How It Works

The SDK implements the following workflow:

  1. Authentication: Uses your API key to authenticate all requests to the Reality Defender API.
  2. File Upload:
    • Requests a presigned URL from the Reality Defender API
    • Uploads the file directly to the storage provider using the presigned URL
    • Returns a request ID for tracking the analysis
  3. Result Retrieval:
    • Polls the API for results using the request ID
    • Optionally waits until the analysis is complete
    • Returns detailed analysis results including overall and model-specific scores

API Reference

See the documentation for complete API details.

Development

Prerequisites

  • Rust 1.56 or later
  • Cargo

Setup

  1. Clone the repository
  2. Install dependencies:
cargo build

Running Tests

cargo test

Running with Coverage

cargo install cargo-tarpaulin
cargo tarpaulin --out Xml

Dependencies

~7–19MB
~253K SLoC