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README.md

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+---
+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.