docs/detector/note-for-backend.md

# 📦API integration note

## Overview

This system integrates **three image forensics methods**—**ELA**, **FFT**, and **Metadata analysis**—into a single detection pipeline to determine whether an image is AI-generated, manipulated, or authentic.

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## 🔍 Detection Modules

### 1. **ELA (Error Level Analysis)**

* **Purpose:** Detects tampering or editing by analyzing compression error levels.
* **Accuracy:** ✅ *Most accurate method*
* **Performance:** ❗ *Slowest method*
* **Output:** `True` (edited) or `False` (authentic)

### 2. **FFT (Fast Fourier Transform)**

* **Purpose:** Identifies high-frequency patterns typical of AI-generated images.
* **Accuracy:** ⚠️ *Moderately accurate*
* **Performance:** ❗ *Moderate to slow*
* **Output:** `True` (likely AI-generated) or `False` (authentic)

### 3. **Metadata Analysis**

* **Purpose:** Detects traces of AI tools or editors in image metadata or binary content.
* **Accuracy:** ⚠️ *Fast but weaker signal*
* **Performance:** 🚀 *Fastest method*
* **Output:** One of:

  * `"ai_generated"` – AI tool or generator identified
  * `"edited"` – Edited using known software
  * `"undetermined"` – No signature found

---

## 🧩 Integration Plan

### ➕ Combine all three APIs into one unified endpoint:

```bash
POST /api/detect-image
```

### Input:

* `image`: Image file (binary, any format supported by Pillow)

### Output:

```json
{
  "ela_result": true,
  "fft_result": false,
  "metadata_result": "ai_generated",
  "final_decision": "ai_generated" 
}
```
> NOTE:Optionally recommending a default logic (e.g., trust ELA > FFT > Metadata).

## Result implementation
| `ela_result` | `fft_result` | `metadata_result` | Suggested Final Decision | Notes                                                                   |
| ------------ | ------------ | ----------------- | ------------------------ | ----------------------------------------------------------------------- |
| `true`       | `true`       | `"ai_generated"`  | `ai_generated`           | Strong evidence from all three modules                                  |
| `true`       | `false`      | `"edited"`        | `edited`                 | ELA confirms editing, no AI signals                                     |
| `true`       | `false`      | `"undetermined"`  | `edited`                 | ELA indicates manipulation                                              |
| `false`      | `true`       | `"ai_generated"`  | `ai_generated`           | No edits, but strong AI frequency & metadata signature                  |
| `false`      | `true`       | `"undetermined"`  | `possibly_ai_generated`  | Weak metadata, but FFT indicates possible AI generation                 |
| `false`      | `false`      | `"ai_generated"`  | `ai_generated`           | Metadata alone shows AI use                                             |
| `false`      | `false`      | `"edited"`        | `possibly_edited`        | Weak signal—metadata shows editing but no structural or frequency signs |
| `false`      | `false`      | `"undetermined"`  | `authentic`              | No detectable manipulation or AI indicators                             |


### Decision Logic:

* Use **ELA** as the **primary indicator** for manipulation.
* Supplement with **FFT** and **Metadata** to improve reliability.
* Combine using a simple rule-based or voting system.

---

## ⚙️ Performance Consideration

| Method   | Speed       | Strength             |
| -------- | ----------- | -------------------- |
| ELA      | ❗ Slow      | ✅ Highly accurate    |
| FFT      | ⚠️ Moderate | ⚠️ Somewhat reliable |
| Metadata | 🚀 Fast     | ⚠️ Low confidence    |

> For high-throughput systems, consider running Metadata first and conditionally applying ELA/FFT if suspicious.