README.md

---
license: mit
tags:
- medical-imaging
- chest-xray
- pneumonia-detection
- efficientnet
- pytorch
- adversarial-ai
pipeline_tag: image-classification
---

# adversarial-ai-target

EfficientNet-B3 fine-tuned for binary chest X-ray classification.
Built as the primary attack target for the [adversarial-ai-attacks-mitigations](https://github.com/emsikes/adversarial-ai-attacks-mitigations) research series.

## Model Details

| Property | Value |
|---|---|
| Architecture | EfficientNet-B3 (ImageNet pretrained) |
| Task | Binary image classification |
| Classes | NORMAL (0), PNEUMONIA (1) |
| Input size | 300 × 300 RGB |
| Framework | PyTorch 2.0 |
| Dataset | [Kaggle chest-xray-pneumonia](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia) |

## Training

| Property | Value |
|---|---|
| Phase 1 (epochs 1-4) | Backbone frozen, head only, lr=1e-3 |
| Phase 2 (epochs 5-10) | Last 3 backbone blocks unfrozen, lr=1e-4 |
| Optimizer | AdamW |
| Scheduler | CosineAnnealingLR |
| Batch size | 64 (A100) |
| Class balancing | WeightedRandomSampler |

## Performance

| Metric | Value |
|---|---|
| Test Accuracy | 0.8862 |
| AUC | 0.9738 |
| PNEUMONIA Recall | 0.99 |
| NORMAL Precision | 0.99 |

## Intended Use

This model is intended strictly for adversarial AI security research and education.
It serves as the attack surface for chapters 4-9 and 12 of the hands-on lab series
covering poisoning attacks, evasion attacks, model extraction, membership inference,
and GAN-based attacks.

**Do not use this model for clinical decision making.**

## Research Series

Part of [The Inference Loop](https://theinferenceloop.substack.com) research series.