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Leverage Explainable AI to understand the reasoning behind AI-generated image detection.
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Analysis Results
Uploaded Images Summary
Overall Summary
[Summary statistics will appear here after analysis]
Technical Details & Performance
Layer-wise Relevance Propagation (LRP)
LRP decomposes the prediction decision backward through the network layers to assign relevance scores to input features.
Attention Mechanism Visualizations
If the model uses attention layers (e.g., in Transformers), this visualizes where the model "focused" when processing the image.
Metadata Analysis Results
Analysis of image metadata (e.g., EXIF) can sometimes reveal clues about image origin or manipulation, if available.
No metadata available or analyzed.
Model Architecture
Overview of the neural network architecture used for detection (e.g., CNN, Vision Transformer).
API Documentation (Example)
Example of how to use the XAI Detector API programmatically.
POST /analyze
Headers:
Content-Type: application/json
Authorization: Bearer YOUR_API_KEY
Body:
{
"image": "base64_encoded_string_or_url"
}
Response (200 OK):
{
"prediction": "AI-Generated",
"confidence_score": 0.975,
"model_used": "EfficientNet-B7 + XAI",
"explanations": {
"saliency_map": "data:image/png;base64,...",
"lime_regions": [...],
"shap_values": [...],
"text_summary": "...",
"counterfactuals": [...]
},
"metrics": { ... }
}
[Note: This is illustrative example documentation.]
Overall Model Performance
| Metric | Value |
|---|---|
| Accuracy | N/A |
| Precision | N/A |
| Recall | N/A |
| F1-Score | N/A |
Metrics based on evaluation dataset.
Comparative Analysis
Compare the subtle differences between a real image and an AI-generated counterpart using the slider. (Placeholder images used).
Slide the handle to reveal more of the AI-generated image overlay.