Add an example of Anomalib adversary

examples/anomalib_adversary/.gitignore

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+results
+datasets

examples/anomalib_adversary/README.md

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+# Adversarial Robustness of Anomaly Detection
+
+This project demonstrates how to generate adversarial examples against anomaly detection models in [Anomalib](https://github.com/openvinotoolkit/anomalib).
+
+## Installation
+
+Anomalib requires Python 3.10+.
+
+```sh
+pip install -e .
+```
+
+## Experiment
+
+0. \[Optional\] Soft link the existing datasets folder from Anomalib if you have downloaded datasets before with Anomalib.
+
+```sh
+ln -s {PATH_TO_ANOMALIB_REPO}/datasets .
+```
+
+1. Train a model. We add an EarlyStopping callback in command line.
+
+```sh
+CUDA_VISIBLE_DEVICES=0 anomalib train \
+--data anomalib.data.MVTec \
+--data.category transistor \
+--model Stfpm \
+--trainer.callbacks lightning.pytorch.callbacks.EarlyStopping \
+--trainer.callbacks.patience 5 \
+--trainer.callbacks.monitor pixel_AUROC \
+--trainer.callbacks.mode max
+```
+
+2. Evaluate the trained model without adversary as baseline.
+
+```sh
+CUDA_VISIBLE_DEVICES=0 anomalib test \
+--data anomalib.data.MVTec \
+--data.category transistor \
+--model Stfpm \
+--ckpt_path=results/Stfpm/MVTec/transistor/latest/weights/lightning/model.ckpt
+```
+
+```console
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Test metric        ┃       DataLoader 0        ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│        image_AUROC        │    0.8733333349227905     │
+│       image_F1Score       │    0.7945205569267273     │
+│        pixel_AUROC        │    0.7860202789306641     │
+│       pixel_F1Score       │    0.46384868025779724    │
+└───────────────────────────┴───────────────────────────┘
+```
+
+3. Generate an adversary config file from MART.
+
+```sh
+python -m mart.generate_config \
+--config_dir="configs" \
+--export_node=callbacks.adversary_connector \
+--resolve=True \
+callbacks=adversary_connector \
+batch_c15n@callbacks.adversary_connector.batch_c15n=dict_imagenet_normalized \
+callbacks.adversary_connector.adversary=$\{attack\} \
++attack=classification_fgsm_linf \
+~attack.gain \
++attack.gain._target_=mart.nn.Get \
++attack.gain.key=loss \
+attack.objective=null \
+attack.eps=10 \
+attack.callbacks.progress_bar.enable=true \
+> anomalib_fgsm_linf_10.yaml
+```
+
+4. Run attack. We add a MART callback that loads the attack config file we just generated [./anomalib_fgsm_linf_10.yaml](./anomalib_fgsm_linf_10.yaml).
+
+```sh
+CUDA_VISIBLE_DEVICES=0 anomalib test \
+--data anomalib.data.MVTec \
+--data.category transistor \
+--model Stfpm \
+--trainer.callbacks mart.utils.CallbackInstantiator \
+--trainer.callbacks.cfg_path ./anomalib_fgsm_linf_10.yaml \
+--ckpt_path=results/Stfpm/MVTec/transistor/latest/weights/lightning/model.ckpt
+```
+
+```console
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Test metric        ┃       DataLoader 0        ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│        image_AUROC        │    0.5979167222976685     │
+│       image_F1Score       │    0.5714285969734192     │
+│        pixel_AUROC        │     0.686780571937561     │
+│       pixel_F1Score       │    0.0955422893166542     │
+└───────────────────────────┴───────────────────────────┘
+```

examples/anomalib_adversary/pyproject.toml

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+[project]
+name = "anomalib_adversary"
+version = "0.1.0a"
+description = "Evaluating robustness of anomaly detection models in Anomalib."
+authors = [
+  {name = "Intel Corporation"}
+]
+
+dependencies = [
+    "anomalib[full]==1.0.1",
+    "mart @ https://github.com/IntelLabs/MART/archive/refs/tags/v0.6.1.zip",
+]
+
+[tool.setuptools.packages.find]
+include = ["anomalib_adversary*"]