diff --git a/.github/workflows/coverage_badge_on_merge.yml b/.github/workflows/coverage_badge_on_merge.yml index f0a6130..ded8346 100644 --- a/.github/workflows/coverage_badge_on_merge.yml +++ b/.github/workflows/coverage_badge_on_merge.yml @@ -33,7 +33,9 @@ jobs: - name: Install uv and create environment run: | pip install uv - uv venv + # Only create the venv on a cache miss; the cache step may have + # already restored a populated .venv, and `uv venv` errors if one exists. + [ -d .venv ] || uv venv source .venv/bin/activate uv sync --extra dev uv pip install auto-verify diff --git a/.github/workflows/run_pytests_when_PR_opened.yml b/.github/workflows/run_pytests_when_PR_opened.yml index cbca501..6be6071 100644 --- a/.github/workflows/run_pytests_when_PR_opened.yml +++ b/.github/workflows/run_pytests_when_PR_opened.yml @@ -36,7 +36,9 @@ jobs: - name: Install uv and create environment run: | pip install uv - uv venv + # Only create the venv on a cache miss; the cache step may have + # already restored a populated .venv, and `uv venv` errors if one exists. + [ -d .venv ] || uv venv source .venv/bin/activate uv sync --extra dev uv pip install auto-verify diff --git a/README.md b/README.md index 9b06c99..22617b9 100644 --- a/README.md +++ b/README.md @@ -138,6 +138,10 @@ This package makes use of the following tools and libraries: - **auto-verify** ([GitHub](https://github.com/ADA-research/auto-verify)) - For integrating verifiers [nnenum](https://github.com/stanleybak/nnenum), [AB-Crown](https://github.com/Verified-Intelligence/alpha-beta-CROWN), [VeriNet](https://github.com/vas-group-imperial/VeriNet), and [Oval-Bab](https://github.com/oval-group/oval-bab). Please refer to the auto-verify [documentation](https://ada-research.github.io/auto-verify/) for details about auto-verify. + +- **foolbox** ([GitHub](https://github.com/bethgelab/foolbox)) + - Rauber, J., Brendel, W., and Bethge, M., "Foolbox: A Python toolbox to benchmark the robustness of machine learning models," in *Reliable Machine Learning in the Wild Workshop, 34th International Conference on Machine Learning*, 2017. [Online]. Available: http://arxiv.org/abs/1707.04131 + - Rauber, J., Zimmermann, R., Bethge, M., and Brendel, W., "Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX," *Journal of Open Source Software*, vol. 5, no. 53, p. 2607, 2020. [Online]. Available: https://doi.org/10.21105/joss.02607 We thank the authors and maintainers of these projects, as well as the authors and maintainers of the verifiers for their contributions to the robustness research community. diff --git a/ada_verona/__init__.py b/ada_verona/__init__.py index be2ce78..b137fdb 100644 --- a/ada_verona/__init__.py +++ b/ada_verona/__init__.py @@ -87,12 +87,21 @@ stacklevel=2, ) +# Check for foolbox availability +HAS_FOOLBOX = importlib.util.find_spec("foolbox") is not None +if not HAS_FOOLBOX: + warnings.warn( + "Foolbox not found. Some adversarial attack features will be limited. To install: pip install foolbox", + stacklevel=2, + ) + __all__ = [ "__version__", "__author__", "HAS_AUTOATTACK", "HAS_AUTOVERIFY", + "HAS_FOOLBOX", # Core abstract classes "DatasetSampler", "EpsilonValueEstimator", @@ -146,3 +155,8 @@ "parse_counter_example_label", ] ) + +if HAS_FOOLBOX: + foolbox_attack_module = importlib.import_module(".verification_module.attacks.foolbox_attack", __package__) + FoolboxAttack = foolbox_attack_module.FoolboxAttack + __all__.extend(["FoolboxAttack"]) diff --git a/ada_verona/database/experiment_repository.py b/ada_verona/database/experiment_repository.py index 90475d3..182c07a 100644 --- a/ada_verona/database/experiment_repository.py +++ b/ada_verona/database/experiment_repository.py @@ -211,7 +211,8 @@ def get_result_df(self) -> pd.DataFrame: result_df_path = self.get_results_path() / DEFAULT_RESULT_CSV_NAME if result_df_path.exists(): df = pd.read_csv(result_df_path, index_col=0) - df["network"] = df.network_path.str.split("/").apply(lambda x: x[-1]).apply(lambda x: x.split(".")[0]) + if "network" not in df.columns and "network_path" in df.columns: + df["network"] = df.network_path.str.split("/").apply(lambda x: x[-1]).apply(lambda x: x.split(".")[0]) return df else: diff --git a/ada_verona/verification_module/attacks/foolbox_attack.py b/ada_verona/verification_module/attacks/foolbox_attack.py new file mode 100644 index 0000000..5a7b2bf --- /dev/null +++ b/ada_verona/verification_module/attacks/foolbox_attack.py @@ -0,0 +1,128 @@ +# Copyright 2025 ADA Reseach Group and VERONA council. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +import foolbox +from torch import Tensor, nn + +from ada_verona.verification_module.attacks.attack import Attack + + +class FoolboxAttack(Attack): + """ + A wrapper for Foolbox adversarial attacks. + Requires foolbox to be installed: pip install foolbox + + Both untargeted and targeted attacks are supported via the `target` parameter + of `execute()`, which is always the correct class label: + + - Untargeted (default): foolbox's `Misclassification` criterion is used, i.e. + the attack searches for an input no longer classified as `target`. + - Targeted (`targeted=True` plus a `target_class`): foolbox's + `TargetedMisclassification` criterion is used, i.e. the attack searches for + an input classified as `target_class`. + + Targeted mode is attack-dependent: iterative attacks such as `LinfPGD`, + `LinfBasicIterativeAttack` and `LinfDeepFoolAttack` support it, while + single-step attacks such as `LinfFastGradientAttack` (FGSM) do not and raise + `ValueError: unsupported criterion`. Within the One2Any robustness pipeline a + successful targeted attack also implies the true label is no longer predicted, + so it is scored as a (typically harder) counterexample; `target_class` should + therefore differ from the true label, otherwise the search is degenerate. + + Attributes: + attack_cls (class): The Foolbox attack class to use. + kwargs (dict): Arguments to pass to the attack constructor. + """ + + def __init__( + self, attack_cls, bounds=(0, 1), *, targeted: bool = False, target_class: int | None = None, **kwargs + ) -> None: + """ + Initialize the FoolboxAttack wrapper. + + Args: + attack_cls (class): The Foolbox attack class (e.g., foolbox.attacks.LinfPGD). + bounds (tuple, optional): The bounds of the input data. Defaults to (0, 1). + targeted (bool, optional): If True, run the attack in targeted mode. Defaults to False. + target_class (int, optional): The class to drive predictions toward in targeted mode. + Required when ``targeted`` is True; ignored otherwise. + **kwargs: Arguments to be passed to the attack constructor (e.g., steps=40). + """ + super().__init__() + if targeted and target_class is None: + raise ValueError("A targeted FoolboxAttack requires `target_class` (the class to aim predictions at).") + self.attack_cls = attack_cls + self.bounds = bounds + self.targeted = targeted + self.target_class = target_class + self.kwargs = kwargs + mode = f"targeted->{target_class}" if targeted else "untargeted" + self.name = f"FoolboxAttack ({attack_cls.__name__}, bounds={bounds}, {mode}, {kwargs})" + + def execute(self, model: nn.Module, data: Tensor, target: Tensor, epsilon: float) -> Tensor: + """ + Execute the Foolbox attack on the given model and data. + + Args: + model (nn.Module): The model to attack. + data (Tensor): The input data to perturb. + target (Tensor): The target labels for the data. + epsilon (float): The perturbation magnitude. + + Returns: + Tensor: The perturbed data. + """ + fmodel = foolbox.PyTorchModel(model, bounds=self.bounds) + + attack = self.attack_cls(**self.kwargs) + + # Ensure data has batch dimension (Foolbox requires batch dimension) + # Data should be (batch_size, channels, height, width) or (batch_size, features) + # Foolbox expects at least 2D tensors: (batch_size, ...) + if data.dim() == 0: + # Scalar, add batch dimension: (1,) + data = data.unsqueeze(0) + elif data.dim() == 1: + # 1D tensor, add batch dimension: (1, features) + data = data.unsqueeze(0) + elif data.dim() == 3: + # 3D tensor (C, H, W), add batch dimension: (1, C, H, W) + data = data.unsqueeze(0) + # If data is already 4D (B, C, H, W) or 2D (B, features), keep as is + # But verify it has a batch dimension + if data.dim() >= 2 and data.shape[0] == 0: + raise ValueError(f"Data tensor has invalid batch size: {data.shape}") + + # Ensure target has batch dimension + # Target should be 1D with shape (batch_size,) for a single sample: (1,) + if target.dim() == 0: + # Scalar target, add batch dimension + target = target.unsqueeze(0) + elif target.dim() == 1 and target.shape[0] == 0: + raise ValueError("Target tensor cannot be empty") + # If target is already correct shape, keep as is + + # Pick the foolbox criterion: aim away from the true label (untargeted) or + # toward a chosen class (targeted). Foolbox would auto-wrap a bare label + # tensor as Misclassification; we build it explicitly for symmetry. + if self.targeted: + target_classes = target.new_full(target.shape, self.target_class) + criterion = foolbox.criteria.TargetedMisclassification(target_classes) + else: + criterion = foolbox.criteria.Misclassification(target) + + _, clipped_advs, _ = attack(fmodel, data, criterion, epsilons=epsilon) + + return clipped_advs diff --git a/docs/how-to-guides.md b/docs/how-to-guides.md index fe3b77d..3d1347c 100644 --- a/docs/how-to-guides.md +++ b/docs/how-to-guides.md @@ -95,6 +95,7 @@ VERONA implements the following adversarial attack methods: - **Fast Gradient Sign Method (FGSM)** - [Goodfellow et al., 2015](https://arxiv.org/abs/1412.6572) - **Projected Gradient Descent (PGD)** - [Madry et al., 2018](https://arxiv.org/abs/1706.06083) - **AutoAttack** - [Croce and Hein, 2020](https://proceedings.mlr.press/v119/croce20b.html) +- All attacks from [foolbox](https://github.com/bethgelab/foolbox/tree/master/foolbox/attacks) through the [`FoolboxAttack`](../ada_verona/verification_module/attacks/foolbox_attack.py) class ### Optional: AutoAttack Installation diff --git a/examples/scripts/create_robustness_dist_foolbox.py b/examples/scripts/create_robustness_dist_foolbox.py new file mode 100644 index 0000000..40f1d5a --- /dev/null +++ b/examples/scripts/create_robustness_dist_foolbox.py @@ -0,0 +1,241 @@ +# Copyright 2025 ADA Reseach Group and VERONA council. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tutorial: robustness distributions with Foolbox attacks across norms & datasets. + +This walk-through showcases several pieces of VERONA at once: + + * Part 1 -- run three *L-inf* Foolbox attacks (FGSM, PGD, DeepFool) on the + bundled ImageFileDataset and overlay their robustness distributions to see + how the choice of attack changes the estimated robustness. + * Part 2 -- run an *L2* attack to show that the pipeline is norm-agnostic: only + the attack class and the epsilon scale change (L2 budgets are much larger + than L-inf ones). This part also loads its data via PytorchExperimentDataset + (a torchvision dataset) instead of ImageFileDataset, to demonstrate that + loader. + +Background -- what is a "robustness distribution"? +-------------------------------------------------- +For every (correctly classified) input we binary-search for the *critical +epsilon*: the smallest perturbation budget at which the attack flips the +prediction. Collecting that critical epsilon over many inputs gives a +distribution; its empirical CDF (ECDF) is the robustness distribution. + +How to read the comparison +--------------------------- +A *stronger* attack finds adversarial examples with *smaller* perturbations, so +its ECDF sits further to the *left / higher*; a *weaker* attack pushes its curve +to the right. Since attacks only upper-bound the true robustness, the left-most +curve is the most informative. + +NOTE -- plotting dependency +--------------------------- +The comparison plots use the grouping-agnostic ReportCreator (``group_by=...``), +which is added in branch ``henba1-enhance-report-creator`` (commit +53f7c65093adcdaa7b7b639628d1247c14dbeb1e). Until that change is merged, run this +script with that version of ``ada_verona.analysis.report_creator`` on the path. + +How to run +---------- + cd examples/scripts + python create_robustness_dist_foolbox.py + +Requires ``foolbox`` (``pip install foolbox``). Outputs (CSVs + PNG plots) are +written next to the example experiment under ``results_foolbox_comparison/``. +""" + +import importlib.util + +# ── Step 0: make sure the optional dependency is available ───────────────────── +# Foolbox is an *optional* extra for VERONA, so fail early with a helpful message +# rather than with a cryptic ImportError halfway through the run. +if importlib.util.find_spec("foolbox") is None: + raise ImportError("Foolbox not found. This package is required for this script. To install: pip install foolbox") + +import logging +from pathlib import Path + +import numpy as np +import pandas as pd +import torch +import torchvision +import torchvision.transforms as transforms +from foolbox.attacks import L2PGD, LinfDeepFoolAttack, LinfFastGradientAttack, LinfPGD + +import ada_verona.util.logger as logger +from ada_verona.analysis.report_creator import ReportCreator +from ada_verona.database.dataset.image_file_dataset import ImageFileDataset +from ada_verona.database.dataset.pytorch_experiment_dataset import PytorchExperimentDataset +from ada_verona.database.experiment_repository import ExperimentRepository +from ada_verona.dataset_sampler.predictions_based_sampler import PredictionsBasedSampler +from ada_verona.epsilon_value_estimator.binary_search_epsilon_value_estimator import ( + BinarySearchEpsilonValueEstimator, +) +from ada_verona.verification_module.attack_estimation_module import AttackEstimationModule +from ada_verona.verification_module.attacks.foolbox_attack import FoolboxAttack +from ada_verona.verification_module.property_generator.one2any_property_generator import ( + One2AnyPropertyGenerator, +) + +logger.setup_logging(level=logging.INFO) +torch.manual_seed(0) # PGD uses a random start; seed for reproducible curves + +# ── Step 1: the three L-inf attacks to compare ───────────────────────────────── +# Each entry is (label, foolbox_attack_class, attack_kwargs). We deliberately span +# the spectrum from "cheap & weak" to "strong" to "minimal-perturbation": +# * FGSM -- a single gradient step; weakest here, so it over-estimates +# robustness (curve furthest right). +# * PGD -- iterated FGSM projected back into the L-inf ball; a strong, +# widely used baseline (usually the left-most curve). +# * DeepFool -- walks toward the nearest decision boundary (minimal perturbation). +# Any untargeted L-inf Foolbox attack works -- just add it to this list. +LINF_ATTACKS = [ + ("FGSM", LinfFastGradientAttack, {}), + ("PGD", LinfPGD, {"steps": 40}), + ("DeepFool", LinfDeepFoolAttack, {"steps": 50}), +] + +# ── Step 2: experiment configuration ─────────────────────────────────────────── +# Anchor every path to *this file* so the script runs from any working directory. +SCRIPT_DIR = Path(__file__).resolve().parent +EXAMPLE_DIR = SCRIPT_DIR.parent / "example_experiment" + +results_root = EXAMPLE_DIR / "results_foolbox_comparison" +network_folder = EXAMPLE_DIR / "data" / "networks" +image_folder = EXAMPLE_DIR / "data" / "images" +image_label_file = EXAMPLE_DIR / "data" / "image_labels.csv" + +timeout = 600 +# Epsilon grids the binary search snaps to. NOTE the very different scales: an +# L-inf budget bounds the *per-pixel* change (small), whereas an L2 budget bounds +# the *whole-image* Euclidean norm (much larger). Using an L-inf grid for an L2 +# attack (or vice-versa) would make every input look trivially (non-)robust. +linf_epsilon_list = np.arange(0.00, 0.4, 0.0039) +l2_epsilon_list = np.arange(0.00, 4.0, 0.04) + +# The sampler and property generator are shared everywhere. We only attack inputs +# the network already classifies correctly (flipping a wrong prediction is moot). +dataset_sampler = PredictionsBasedSampler(sample_correct_predictions=True) +property_generator = One2AnyPropertyGenerator() + + +def run_attack_experiment(label, attack_cls, attack_kwargs, dataset, epsilon_list) -> pd.DataFrame: + """Run the full VERONA pipeline for a single attack/dataset and return results. + + Args: + label: Human-readable name used for the experiment folder and plot legend. + attack_cls: The Foolbox attack class to wrap. + attack_kwargs: Keyword arguments forwarded to the attack constructor. + dataset: The ExperimentDataset to evaluate on. + epsilon_list: The (norm-appropriate) epsilon grid for the binary search. + + Returns: + The per-input result DataFrame, tagged with an ``attack`` column. + """ + print(f"\n{'=' * 70}\nRunning attack: {label} ({attack_cls.__name__}) with kwargs={attack_kwargs}\n{'=' * 70}") + + # Each attack gets its own experiment folder so results never clobber each + # other; re-running the script overwrites the previous run cleanly. + experiment_name = f"foolbox_{label}" + file_database = ExperimentRepository(base_path=results_root, network_folder=network_folder) + file_database.initialize_new_experiment(experiment_name) + file_database.save_configuration( + dict( + experiment_name=experiment_name, + attack=attack_cls.__name__, + attack_kwargs=attack_kwargs, + network_folder=str(network_folder), + dataset=str(dataset), + timeout=timeout, + epsilon_list=[str(x) for x in epsilon_list], + ) + ) + + # Wrap the Foolbox attack and plug it into the estimation pipeline. The binary + # search calls the attack repeatedly to home in on the critical epsilon. + verifier = AttackEstimationModule(attack=FoolboxAttack(attack_cls, bounds=(0, 1), **attack_kwargs)) + epsilon_value_estimator = BinarySearchEpsilonValueEstimator( + epsilon_value_list=epsilon_list.copy(), verifier=verifier + ) + + network_list = file_database.get_network_list() + print(f"Found {len(network_list)} network(s).") + + for network in network_list: + print(f" Processing network: {network.name}") + sampled_data = dataset_sampler.sample(network, dataset) + print(f" Sampled {len(sampled_data)} data point(s).") + + for i, data_point in enumerate(sampled_data): + verification_context = file_database.create_verification_context(network, data_point, property_generator) + epsilon_value_result = epsilon_value_estimator.compute_epsilon_value(verification_context) + print(f" [{label}] data point {i}: critical epsilon = {epsilon_value_result.epsilon}") + file_database.save_result(epsilon_value_result) + + # Native per-experiment plots (hist/box/kde/ecdf) written alongside the CSV. + file_database.save_plots() + + result_df = file_database.get_result_df() + result_df["attack"] = label + return result_df + + +# ── Step 3: Part 1 -- L-inf comparison on the bundled ImageFileDataset ────────── +# The example image dataset is small, so we evaluate every sampled input (no +# early stopping) to get the smoothest possible curves. +image_dataset = ImageFileDataset(image_folder=image_folder, label_file=image_label_file) +linf_results = [ + run_attack_experiment(label, cls, kwargs, image_dataset, linf_epsilon_list) for label, cls, kwargs in LINF_ATTACKS +] + +combined_df = pd.concat(linf_results, ignore_index=True) +results_root.mkdir(parents=True, exist_ok=True) +combined_csv = results_root / "combined_results.csv" +combined_df.to_csv(combined_csv, index=False) +print(f"\nWrote combined L-inf results to {combined_csv}") + +# Overlay the three L-inf attacks on one robustness distribution (grouped by attack). +report = ReportCreator( + combined_df, + group_by="attack", + value_label="Critical epsilon (L-inf perturbation budget)", +) +ecdf_path = results_root / "comparison_ecdf.png" +box_path = results_root / "comparison_boxplot.png" +report.create_ecdf_figure().savefig(ecdf_path, bbox_inches="tight") +report.create_box_figure().savefig(box_path, bbox_inches="tight") +print(f"Saved L-inf comparison plots:\n {ecdf_path}\n {box_path}") + +print("\nMean critical epsilon per L-inf attack (smaller = stronger attack):") +for attack_name, mean_eps in combined_df.groupby("attack")["epsilon_value"].mean().sort_values().items(): + print(f" {attack_name:<10} {mean_eps:.4f}") + +# ── Step 4: Part 2 -- an L2 attack on a torchvision dataset ───────────────────── +# Exactly the same pipeline; only two things change: +# 1. the attack is an L2 attack (so we use the larger L2 epsilon grid), and +# 2. the data comes from a torchvision dataset wrapped in PytorchExperimentDataset +# instead of ImageFileDataset. +# MNIST is large, so we take a small subset (via get_subset) to keep the demo quick. +print(f"\n{'#' * 70}\nPart 2: L2 attack on a torchvision (PytorchExperimentDataset) MNIST subset\n{'#' * 70}") +torch_dataset = torchvision.datasets.MNIST( + root=str(EXAMPLE_DIR / "data" / "pytorch_mnist"), + train=False, + download=True, + transform=transforms.ToTensor(), +) +pytorch_dataset = PytorchExperimentDataset(dataset=torch_dataset).get_subset(list(range(50))) + +run_attack_experiment("L2PGD", L2PGD, {"steps": 40}, pytorch_dataset, l2_epsilon_list) + +print("\nDone. Open the PNGs under results_foolbox_comparison/ to inspect the distributions.") diff --git a/pyproject.toml b/pyproject.toml index ac54287..d60a47b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,6 +46,10 @@ dependencies = [ [project.optional-dependencies] +foolbox = [ + "foolbox>=3.3.4" +] + gpu = [ "nvidia-cublas-cu12>=12.1.0,<13.0.0", "nvidia-cuda-cupti-cu12>=12.1.0,<13.0.0", @@ -68,6 +72,7 @@ dev = [ "pytest-cov>=4.1.0", "coverage>=7.0.0", "auto-verify==1.0.0", + "foolbox>=3.3.4", "autoattack @ git+https://github.com/fra31/auto-attack", "ruff>=0.3.4", "black>=22.0.0", @@ -121,4 +126,10 @@ select = [ "SIM", # isort "I", -] \ No newline at end of file +] + +[tool.ruff.lint.isort] +known-first-party = ["ada_verona"] +# foolbox is a third-party dependency; pin it explicitly so a local foolbox/ +# source checkout in the repo root cannot be misdetected as first-party. +known-third-party = ["foolbox"] \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 9fb2e33..58bcc47 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,15 +1,157 @@ -# VERONA Requirements - CPU-only installation -# Note: NVIDIA packages are excluded for compatibility with different CUDA versions. -# For direct GPU support, use: uv pip install ada-verona -matplotlib>=3.10.0 -numpy>=1.24.3 -onnx>=1.14.0 -onnxruntime>=1.14.1 -onnx2torch>=1.5.14 -pandas>=2.0.1 -PyYAML>=6.0.1 -result>=0.9.0 -seaborn>=0.13.2 -torch>=2.6.0 -torchvision>=0.18.1 -typing_extensions \ No newline at end of file +# This file was autogenerated by uv via the following command: +# uv pip compile /gpfs/home2/jvrijn/code/VERONA/pyproject.toml -o /gpfs/home2/jvrijn/code/VERONA/requirements.txt +coloredlogs==15.0.1 + # via onnxruntime +contourpy==1.3.2 + # via matplotlib +cuda-bindings==13.3.1 + # via torch +cuda-pathfinder==1.5.5 + # via cuda-bindings +cuda-toolkit==13.0.2 + # via torch +cycler==0.12.1 + # via matplotlib +filelock==3.29.4 + # via torch +flatbuffers==25.12.19 + # via onnxruntime +fonttools==4.63.0 + # via matplotlib +fsspec==2026.6.0 + # via torch +humanfriendly==10.0 + # via coloredlogs +jinja2==3.1.6 + # via torch +kiwisolver==1.5.0 + # via matplotlib +markupsafe==3.0.3 + # via jinja2 +matplotlib==3.10.9 + # via + # ada-verona (pyproject.toml) + # seaborn +ml-dtypes==0.5.4 + # via onnx +mpmath==1.3.0 + # via sympy +networkx==3.4.2 + # via torch +numpy==2.2.6 + # via + # ada-verona (pyproject.toml) + # contourpy + # matplotlib + # ml-dtypes + # onnx + # onnx2torch + # onnxruntime + # pandas + # seaborn + # torchvision +nvidia-cublas==13.1.1.3 + # via + # nvidia-cudnn-cu13 + # nvidia-cusolver + # torch +nvidia-cuda-cupti==13.0.85 + # via cuda-toolkit +nvidia-cuda-nvrtc==13.0.88 + # via + # cuda-toolkit + # nvidia-cublas +nvidia-cuda-runtime==13.0.96 + # via cuda-toolkit +nvidia-cudnn-cu13==9.20.0.48 + # via torch +nvidia-cufft==12.0.0.61 + # via cuda-toolkit +nvidia-cufile==1.15.1.6 + # via cuda-toolkit +nvidia-curand==10.4.0.35 + # via cuda-toolkit +nvidia-cusolver==12.0.4.66 + # via cuda-toolkit +nvidia-cusparse==12.6.3.3 + # via + # cuda-toolkit + # nvidia-cusolver +nvidia-cusparselt-cu13==0.8.1 + # via torch +nvidia-nccl-cu13==2.29.7 + # via torch +nvidia-nvjitlink==13.0.88 + # via + # cuda-toolkit + # nvidia-cufft + # nvidia-cusolver + # nvidia-cusparse +nvidia-nvshmem-cu13==3.4.5 + # via torch +nvidia-nvtx==13.0.85 + # via cuda-toolkit +onnx==1.22.0 + # via + # ada-verona (pyproject.toml) + # onnx2torch +onnx2torch==1.5.15 + # via ada-verona (pyproject.toml) +onnxruntime==1.23.2 + # via ada-verona (pyproject.toml) +packaging==26.2 + # via + # matplotlib + # onnxruntime +pandas==2.3.3 + # via + # ada-verona (pyproject.toml) + # seaborn +pillow==12.2.0 + # via + # matplotlib + # torchvision +protobuf==7.35.1 + # via + # onnx + # onnxruntime +pyparsing==3.3.2 + # via matplotlib +python-dateutil==2.9.0.post0 + # via + # matplotlib + # pandas +pytz==2026.2 + # via pandas +pyyaml==6.0.3 + # via ada-verona (pyproject.toml) +result==0.17.0 + # via ada-verona (pyproject.toml) +seaborn==0.13.2 + # via ada-verona (pyproject.toml) +setuptools==81.0.0 + # via torch +six==1.17.0 + # via python-dateutil +sympy==1.14.0 + # via + # onnxruntime + # torch +torch==2.12.0 + # via + # ada-verona (pyproject.toml) + # onnx2torch + # torchvision +torchvision==0.27.0 + # via + # ada-verona (pyproject.toml) + # onnx2torch +triton==3.7.0 + # via torch +typing-extensions==4.15.0 + # via + # ada-verona (pyproject.toml) + # onnx + # torch +tzdata==2026.2 + # via pandas diff --git a/tests/test_verification_module/attacks/conftest.py b/tests/test_verification_module/attacks/conftest.py index 6a7d532..c845894 100644 --- a/tests/test_verification_module/attacks/conftest.py +++ b/tests/test_verification_module/attacks/conftest.py @@ -13,12 +13,14 @@ # limitations under the License. # ============================================================================== +import foolbox as fb import pytest import torch from torch import nn from ada_verona.verification_module.attacks.auto_attack_wrapper import AutoAttackWrapper from ada_verona.verification_module.attacks.fgsm_attack import FGSMAttack +from ada_verona.verification_module.attacks.foolbox_attack import FoolboxAttack from ada_verona.verification_module.attacks.pgd_attack import PGDAttack @@ -58,3 +60,8 @@ def pgd_attack(): @pytest.fixture def fgsm_attack(): return FGSMAttack() + + +@pytest.fixture +def foolbox_attack(): + return FoolboxAttack(attack_cls=fb.attacks.LinfFastGradientAttack) diff --git a/tests/test_verification_module/attacks/test_foolbox_attack.py b/tests/test_verification_module/attacks/test_foolbox_attack.py new file mode 100644 index 0000000..895bee1 --- /dev/null +++ b/tests/test_verification_module/attacks/test_foolbox_attack.py @@ -0,0 +1,142 @@ +# Copyright 2025 ADA Reseach Group and VERONA council. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +import foolbox as fb +import pytest +import torch +from torch import nn + +from ada_verona.verification_module.attacks.foolbox_attack import FoolboxAttack + + +def test_foolbox_attack_init_stores_configuration(): + attack = FoolboxAttack(attack_cls=fb.attacks.LinfFastGradientAttack, bounds=(-1, 1), steps=7) + assert attack.attack_cls is fb.attacks.LinfFastGradientAttack + assert attack.bounds == (-1, 1) + assert attack.kwargs == {"steps": 7} + # The name should expose the attack class, bounds and kwargs for traceability. + assert "LinfFastGradientAttack" in attack.name + assert "bounds=(-1, 1)" in attack.name + assert "steps" in attack.name + + +def test_foolbox_attack_execute(foolbox_attack, model, data, target): + epsilon = 0.1 + normalized_data = torch.sigmoid(data) + perturbed_data = foolbox_attack.execute(model, normalized_data, target, epsilon) + assert isinstance(perturbed_data, torch.Tensor) + assert perturbed_data.shape == normalized_data.shape + assert torch.all(perturbed_data >= 0) and torch.all(perturbed_data <= 1) + + +def test_foolbox_attack_execute_3d_data(foolbox_attack, target): + epsilon = 0.1 + + class FlattenModel(nn.Module): + def __init__(self): + super().__init__() + self.fc = nn.Linear(10, 2) + + def forward(self, x): + x = x.view(x.size(0), -1) + return self.fc(x) + + model = FlattenModel() + data_3d = torch.randn(1, 1, 10) + normalized_data = torch.sigmoid(data_3d) + perturbed_data = foolbox_attack.execute(model, normalized_data, target, epsilon) + assert isinstance(perturbed_data, torch.Tensor) + assert perturbed_data.shape == (1, 1, 1, 10) + assert torch.all(perturbed_data >= 0) and torch.all(perturbed_data <= 1) + + +def test_foolbox_attack_execute_0d_target(foolbox_attack, model, data): + epsilon = 0.1 + target_0d = torch.tensor(1) + normalized_data = torch.sigmoid(data) + perturbed_data = foolbox_attack.execute(model, normalized_data, target_0d, epsilon) + assert isinstance(perturbed_data, torch.Tensor) + assert perturbed_data.shape == normalized_data.shape + assert torch.all(perturbed_data >= 0) and torch.all(perturbed_data <= 1) + + +def test_foolbox_attack_execute_1d_data(foolbox_attack, model, target): + epsilon = 0.1 + # 1D input (features only); execute() should add the batch dimension. + data_1d = torch.sigmoid(torch.randn(10)) + perturbed_data = foolbox_attack.execute(model, data_1d, target, epsilon) + assert isinstance(perturbed_data, torch.Tensor) + assert perturbed_data.shape == (1, 10) + assert torch.all(perturbed_data >= 0) and torch.all(perturbed_data <= 1) + + +def test_foolbox_attack_execute_kwargs_passed_to_attack(model, data, target): + # steps controls the number of PGD iterations; passing it should not break execution. + attack = FoolboxAttack(attack_cls=fb.attacks.LinfPGD, steps=3) + perturbed_data = attack.execute(model, torch.sigmoid(data), target, 0.1) + assert isinstance(perturbed_data, torch.Tensor) + assert perturbed_data.shape == data.shape + + +def test_foolbox_attack_execute_empty_data_raises(foolbox_attack, model, target): + # A tensor with batch size 0 is invalid and must be rejected. + empty_data = torch.zeros(0, 10) + with pytest.raises(ValueError): + foolbox_attack.execute(model, empty_data, target, 0.1) + + +def test_foolbox_attack_execute_empty_target_raises(foolbox_attack, model, data): + # A valid batch of data but an empty target tensor must be rejected. + empty_target = torch.zeros(0, dtype=torch.long) + with pytest.raises(ValueError): + foolbox_attack.execute(model, torch.sigmoid(data), empty_target, 0.1) + + +def test_foolbox_attack_execute_0d_data_adds_batch_dimension(target): + # Scalar input exercises the 0D branch, which unsqueezes to a batch dimension. + class SingleFeatureModel(nn.Module): + def __init__(self): + super().__init__() + self.fc = nn.Linear(1, 2) + + def forward(self, x): + return self.fc(x) + + attack = FoolboxAttack(attack_cls=fb.attacks.LinfFastGradientAttack) + # Foolbox cannot run crossentropy on the resulting 1D logits, so a ValueError + # is expected; the assertion guards the 0D unsqueeze branch in execute(). + with pytest.raises(ValueError): + attack.execute(SingleFeatureModel(), torch.tensor(0.5), target, 0.1) + + +def test_foolbox_attack_untargeted_defaults(): + attack = FoolboxAttack(fb.attacks.LinfPGD, steps=5) + assert attack.targeted is False + assert attack.target_class is None + assert "untargeted" in attack.name + + +def test_foolbox_attack_targeted_requires_target_class(): + with pytest.raises(ValueError): + FoolboxAttack(fb.attacks.LinfPGD, targeted=True) + + +def test_foolbox_attack_targeted_execute(model, data, target): + # PGD supports targeted mode; aim at class 0 (the true label fixture is 1). + attack = FoolboxAttack(fb.attacks.LinfPGD, targeted=True, target_class=0, steps=5) + assert "targeted->0" in attack.name + perturbed_data = attack.execute(model, torch.sigmoid(data), target, 0.3) + assert isinstance(perturbed_data, torch.Tensor) + assert perturbed_data.shape == data.shape diff --git a/uv.lock b/uv.lock index 692982b..a8fcac3 100644 --- a/uv.lock +++ b/uv.lock @@ -1,9 +1,10 @@ version = 1 -revision = 3 requires-python = ">=3.10, <3.12" resolution-markers = [ - "python_full_version >= '3.11'", - "python_full_version < '3.11'", + "python_full_version >= '3.11' and platform_system == 'Linux'", + "python_full_version >= '3.11' and platform_system != 'Linux'", + "python_full_version < '3.11' and platform_system == 'Linux'", + "python_full_version < '3.11' and platform_system != 'Linux'", ] [[package]] @@ -11,7 +12,8 @@ name = "ada-verona" version = "1.0.3" source = { editable = "." } dependencies = [ - { name = "matplotlib" }, + { name = "matplotlib", version = "3.10.9", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "matplotlib", version = "3.11.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "numpy" }, { name = "onnx" }, { name = "onnx2torch" }, @@ -32,6 +34,7 @@ dev = [ { name = "black" }, { name = "build" }, { name = "coverage" }, + { name = "foolbox" }, { name = "mypy" }, { name = "pytest" }, { name = "pytest-cov" }, @@ -39,6 +42,9 @@ dev = [ { name = "ruff" }, { name = "twine" }, ] +foolbox = [ + { name = "foolbox" }, +] gpu = [ { name = 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