| --- |
| 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. |
|
|
