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Adversarial Robustness Toolbox v1.0.0
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Adversarial Robustness Toolbox (ART) is a Python library supporting developers and researchers in defending Machine Learning models (Deep Neural Networks, Gradient Boosted Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, Gaussian Processes, Decision Trees, Scikit-learn Pipelines, etc.) against adversarial threats and helps making AI systems more secure and trustworthy. Machine Learning models are vulnerable to adversarial examples, which are inputs (images, texts, tabular data, etc.) deliberately modified to produce a desired response by the Machine Learning model. ART provides the tools to build and deploy defences and test them with adversarial attacks. Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary. The attacks implemented in ART allow creating adversarial attacks against Machine Learning models which is required to test defenses with state-of-the-art threat models. Supported Machine Learning Libraries include TensorFlow (v1 and v2), Keras, PyTorch, MXNet, Scikit-learn, XGBoost, LightGBM, CatBoost, and GPy. The source code of ART is released with MIT license at https://github.com/IBM/adversarial-robustness-toolbox. The release includes code examples, notebooks with tutorials and documentation (http://adversarial-robustness-toolbox.readthedocs.io).
Forward citations
Cited by 9 Pith papers
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Lipschitz-Based Robustness Certification Under Floating-Point Execution
Lipschitz-based robustness certificates that assume real arithmetic can be unsound under floating-point execution; a formal FP-aware theory and certifier close that gap for dense ReLU networks.
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UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks
UnDREAM enables optimization of adversarial textures on arbitrary 3D objects inside Unreal Engine by bridging the simulator to the differentiable renderer Mitsuba.
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Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation
A bilevel optimization framework with curvature-guided geodesic perturbation reduces membership inference attack success while preserving downstream classification accuracy and sample quality.
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DURA-CPS: A Multi-Role Orchestrator for Dependability Assurance in LLM-Enabled Cyber-Physical Systems
DURA-CPS is a multi-role orchestration framework that iteratively assesses safety, security, performance, and recovery of AI components in simulated cyber-physical systems.
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QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits
Hybrid quantum-classical models using structured entanglement keep high accuracy on MNIST, OrganAMNIST and CIFAR-10 while lowering adversarial attack success rates and raising the computational cost of generating attacks.
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Leveraging Trustworthy AI for Automotive Security in Multi-Domain Operations: Towards a Responsive Human-AI Multi-Domain Task Force for Cyber Social Security
Larger Random Forest and Gradient Boosting ensembles increase the time needed for a ZOO black-box attack on a CAN bus IDS, while XGBoost shows no clear relationship.
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An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
A GAN-based adversarial attack is claimed to achieve 99% success in making fraudulent insurance claims look legitimate to LSTM and XGBoost fraud detectors, but the evidence is undermined by missing dataset and impleme...
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SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks
An SDN-based FDDMS with LSTM detects and mitigates false data injection in CAN networks and claims robustness against four adversarial attacks.
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Securing AI Systems: A Guide to Known Attacks and Impacts
A practitioner-oriented review that organizes known adversarial attacks on predictive and generative AI systems into eleven types mapped to confidentiality, integrity, and availability impacts.
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