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Trusted-AI/adversarial-robustness-toolbox

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Adversarial Robustness Toolbox

The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating, defending, and certifying machine learning models against adversarial threats. It wraps models from any framework behind a common estimator interface, enabling composable pipelines for attack generation, defense application, robustness certification, and privacy auditing across evasion, poisoning, and extraction threats.

The library distinguishes itself by covering the full adversarial ML security lifecycle within a single toolkit. It supports gradient-based adversarial example generation, model extraction via substitute training, poisoning detection through spectral signatures, and inference privacy auditing. ART also provides robustness certification through randomized smoothing, preprocessing-based input sanitization, and the ability to chain attacks and defenses into reusable pipelines that operate on any wrapped estimator.

Beyond its core security capabilities, ART works with models built in TensorFlow, PyTorch, scikit-learn, and other frameworks without requiring code changes. It includes support for adversarial training workflows, evasion and poisoning attack evaluation, and privacy attack prevention across classification, regression, object detection, speech recognition, and video tracking tasks. The library is available as a Python package with documentation covering installation, API references, and usage examples.

Features

  • Adversarial Machine Learning - Provides a unified library for testing and hardening machine learning models against evasion, poisoning, and extraction attacks.
  • Adversarial Example Generations - Crafts evasion attacks by computing input gradients through the model and perturbing along the direction that maximizes loss.
  • Extraction Model Evaluation - Tests model resilience to attackers reconstructing a functional copy by querying it.
  • Model Extraction Attack Evaluations - Tests model resilience to adversaries reconstructing a functional copy by querying it.

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6,056 estrellas·1,321 forks·Python·MIT·11 vistas
  • Model Functionality Extraction - Replicates target model behavior by querying it and training a substitute on the responses.
  • Adversarial Robustness Testing - Generates adversarial examples using PGD, FGSM, and DeepFool to test model prediction robustness.
  • Adversarial Robustness Libraries - Provides a unified library for testing and hardening models against adversarial attacks across multiple ML frameworks.
  • Adversarial Example Augmentation - Generates adversarial examples during training to harden models against crafted input perturbations.
  • Adversarial Robustness Training - Trains models with adversarial examples to improve classification accuracy on subtly perturbed inputs.
  • Adversarial Robustness Trainings - Augments training data with adversarial examples to improve a model's resistance to crafted inputs during inference.
  • Randomized Smoothing Certifications - Applies noise injection and statistical bounds to produce provable robustness guarantees around a model's predictions.
  • Multi-Framework Model Collections - Works with models built in TensorFlow, PyTorch, scikit-learn, and other popular frameworks without requiring code changes.
  • Robustness Certification Tools - Provides formal verification and randomized smoothing to certify model predictions within defined perturbation bounds.
  • Robustness Certifications - Provides formal guarantees that a model's predictions remain stable within a defined perturbation radius around any input.
  • Inference Attack Evaluations - Tests model resilience to adversaries inferring sensitive attributes or training data membership.
  • Adversarial Attacks - Provides a framework for generating evasion, poisoning, and extraction attacks to stress-test model robustness.
  • Attack-Defense Pipelines - Provides composable pipelines that chain attack generation, defense application, and robustness evaluation on any wrapped estimator.
  • Adversarial Input Detection - Analyzes inputs and activations to flag samples crafted to deceive the model.
  • Adversarial Threat Defenses - Applies preprocessing, postprocessing, and detection techniques to harden models against multiple attack types.
  • Evasion Attack Evaluations - Tests model resilience to adversarial inputs designed to cause misclassification during inference.
  • Evasion Attack Generators - Generates evasion attacks against classification models, optimizing strength on correctly classified samples.
  • Model Extraction Prevention - Simulates model extraction attacks and provides countermeasures to protect intellectual property.
  • Adversarial Input Sanitization - Applies transformations like compression and smoothing to strip adversarial perturbations before inference.
  • Inference Attack Prevention - Identifies and blocks attempts to infer sensitive information like membership or attributes from a model.
  • Model Inference Attacks - Extracts private training data attributes or membership status by analyzing a model's predictions.
  • ML Framework Abstractions - Applies adversarial robustness techniques across models built in TensorFlow, PyTorch, scikit-learn, and other frameworks without code changes.
  • Framework Abstraction Layers - Wraps models from different frameworks behind a common estimator interface so attacks and defenses work without framework-specific code.
  • Classification Models - Wraps classification models from Keras, PyTorch, and scikit-learn under a unified estimator interface.
  • Training Data Reconstruction Attacks - Recovers missing rows of a training dataset by exploiting a model's learned representations.
  • Object Detection - Ships object detection estimators for architectures like Faster-RCNN and YOLO under a unified interface.
  • Regression Models - Wraps regression estimators from Keras, PyTorch, and scikit-learn under a unified interface.
  • Speech to Text Transcription - Runs automatic speech recognition on audio using PyTorch or TensorFlow estimators.
  • Video Object Tracking - Runs object tracking on video frames using a PyTorch estimator for the GOTURN architecture.
  • Privacy Attack Evaluations - Assesses a model's vulnerability to inference, membership, and reconstruction attacks that leak sensitive training data.
  • Model Extractions - Replicates a target model's behavior by querying it and training a local surrogate on the collected predictions.
  • Poisoning Attack Implementations - Injects malicious data into the training set to compromise a model's behavior, including backdoors and clean-label triggers.
  • Spectral Signature Detections - Detects and mitigates malicious training data using spectral signatures and other poisoning defense techniques.
  • Poisoning Attack Evaluations - Tests a model's resilience to corrupted training data that degrades its performance or introduces backdoors.
  • Poisoning Detections - Identifies and mitigates malicious training data injected to corrupt model behavior or introduce backdoors.
  • Secure - Applies transformations to raw predictions to obscure confidence scores or reduce information leakage to attackers.
  • Poisoning Detections - Identifies malicious training samples by analyzing covariance structure and outlier scores in the feature space.
  • Privacy Audits - Probes a model's outputs to infer membership or attribute information, quantifying leakage risk.
  • Model Extraction Protections - Simulates and counters model extraction attacks to protect intellectual property in deployed ML systems.
  • Preprocessing Sanitizations - Transforms raw inputs through compression, smoothing, or encoding to strip adversarial perturbations before inference.
  • AI Security - Library for testing ML model robustness against adversarial attacks.
  • AI Security and Red Teaming - Python library for adversarial machine learning.
  • Frameworks and Benchmarks - General-purpose library for adversarial and backdoor robustness testing.
  • Sample Filtering - Provides activation clustering to detect backdoors in deep networks.
  • Research Toolkits - Library for adversarial robustness and backdoor defense evaluation.
  • Guardrails and AI Safety - Listed in the “Guardrails and AI Safety” section of the The Incredible Pytorch awesome list.
  • Privacy and Safety - Library for defending and evaluating ML model robustness.
  • Seguridad y privacidad - Security library for machine learning models.

    • Historial de estrellas

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    Preguntas frecuentes

    ¿Qué hace trusted-ai/adversarial-robustness-toolbox?

    The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating, defending, and certifying machine learning models against adversarial threats. It wraps models from any framework behind a common estimator interface, enabling composable pipelines for attack generation, defense application, robustness certification, and privacy auditing across evasion, poisoning, and extraction threats.

    ¿Cuáles son las características principales de trusted-ai/adversarial-robustness-toolbox?

    Las características principales de trusted-ai/adversarial-robustness-toolbox son: Adversarial Machine Learning, Adversarial Example Generations, Extraction Model Evaluation, Model Extraction Attack Evaluations, Model Functionality Extraction, Adversarial Robustness Testing, Adversarial Robustness Libraries, Adversarial Example Augmentation.

    ¿Qué alternativas de código abierto existen para trusted-ai/adversarial-robustness-toolbox?

    Las alternativas de código abierto para trusted-ai/adversarial-robustness-toolbox incluyen: cleverhans-lab/cleverhans — Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples,… tensorflow/cleverhans — Cleverhans is a TensorFlow adversarial machine learning library that serves as an attack framework, a robustness… llm-attacks/llm-attacks — This repository provides tools and methodologies for studying adversarial attacks on large language models. It focuses… cs231n/cs231n.github.io — This project is a static educational website and comprehensive curriculum focused on computer vision and deep… open-mmlab/mmdetection — This project is a modular research toolkit designed for developing, training, and evaluating deep learning models for… apple/turicreate — This project is an automated machine learning framework and toolkit designed for training and tuning custom models for…

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