📦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.

🔍 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:

POST /api/detect-image

Input:

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

Output:

{
  "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.