Quelles sont les fonctionnalités principales de tensorflow/cleverhans ?

Les fonctionnalités principales de tensorflow/cleverhans sont : Adversarial Frameworks, Gradient-Based Perturbations, Adversarial Robustness Testing, Adversarial Robustness Libraries, Defense Libraries, Adversarial Input Generation, Adversarial Threat Defenses, Automatic Differentiation Engines.

Quelles sont les alternatives open-source à tensorflow/cleverhans ?

Les alternatives open-source à tensorflow/cleverhans incluent : cleverhans-lab/cleverhans — Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples,… trusted-ai/adversarial-robustness-toolbox — The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating,… llm-attacks/llm-attacks — This repository provides tools and methodologies for studying adversarial attacks on large language models. It focuses… giskard-ai/giskard — Giskard is an evaluation framework, testing library, and quality monitoring system for large language models and AI… meta-llama/purplellama — PurpleLlama is a collection of security components and toolkits designed for large language models. It provides… christophm/interpretable-ml-book — This project is a comprehensive educational resource and technical manual focused on interpretable machine learning…

Cleverhans

Cleverhans est une bibliothèque de machine learning adversarial pour TensorFlow qui sert de framework d'attaque, de benchmark de robustesse et de bibliothèque de défense. Elle fournit une collection d'outils pour générer des exemples adversariaux, tester la sécurité des réseaux de neurones et implémenter des mécanismes de protection pour accroître la résilience des modèles face aux entrées malveillantes.

Le projet se concentre sur la création d'entrées perturbées conçues pour tromper les modèles de machine learning afin qu'ils produisent des prédictions incorrectes. Il permet l'évaluation de la stabilité et de la précision des modèles de deep learning lorsqu'ils sont soumis à du bruit adversarial, en fournissant des implémentations de référence d'attaques connues pour identifier les failles de sécurité.

Le toolkit couvre la génération d'exemples adversariaux, la défense des modèles de machine learning et le benchmarking de robustesse des réseaux de neurones. Il utilise une interface agnostique au modèle et des implémentations d'attaques différentiables pour exécuter des perturbations basées sur le gradient et des boucles d'optimisation itératives.

Features

Adversarial Frameworks - Serves as a comprehensive framework for building and benchmarking adversarial attacks on neural networks.
Gradient-Based Perturbations - Calculates input gradients via backpropagation to generate the minimal noise required to deceive a model.
Adversarial Robustness Testing - Measures model stability and accuracy by subjecting neural networks to simulated adversarial attacks.
Adversarial Robustness Libraries - Provides a unified TensorFlow library for testing and hardening ML models against adversarial attacks.
Defense Libraries - Ships a suite of protective mechanisms designed to increase the resilience of models against malicious inputs.
Adversarial Input Generation - Generates malicious input perturbations using reference methods to deceive machine learning models.
Adversarial Threat Defenses - Implements protective mechanisms to harden neural networks against evasion and poisoning attacks.
Automatic Differentiation Engines - Implements automatic differentiation through computational graphs to calculate the gradients necessary for adversarial attacks.
Neural Model Interfaces - Provides a standardized interface that decouples attack logic from specific neural network architectures.
Perturbation Constraint Mappings - Uses projection operations to ensure adversarial perturbations remain within valid image ranges.
Security Testing - Uses reference attack implementations to identify and fix security weaknesses in TensorFlow-based networks.
Adversarial Optimization Loops - Provides iterative optimization loops to refine adversarial noise within a defined perturbation budget.
Adversarial Attack Benchmarks - Provides reference implementations of known attacks to ensure reproducible robustness measurements across models.
Adversarial Security Tools - Library for benchmarking machine learning system vulnerability to adversarial examples.
Computer Vision Libraries - Library for adversarial example attacks and defenses.

Historique des stars

tensorflowcleverhans

Name: tensorflow/cleverhans
Author: tensorflow

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6,443 stars1,399 forksJupyter NotebookMIT8 vues

Cleverhans

Features

Adversarial Frameworks - Serves as a comprehensive framework for building and benchmarking adversarial attacks on neural networks.
Gradient-Based Perturbations - Calculates input gradients via backpropagation to generate the minimal noise required to deceive a model.
Adversarial Robustness Testing - Measures model stability and accuracy by subjecting neural networks to simulated adversarial attacks.
Adversarial Robustness Libraries - Provides a unified TensorFlow library for testing and hardening ML models against adversarial attacks.
Defense Libraries - Ships a suite of protective mechanisms designed to increase the resilience of models against malicious inputs.
Adversarial Input Generation - Generates malicious input perturbations using reference methods to deceive machine learning models.
Adversarial Threat Defenses - Implements protective mechanisms to harden neural networks against evasion and poisoning attacks.
Automatic Differentiation Engines - Implements automatic differentiation through computational graphs to calculate the gradients necessary for adversarial attacks.
Neural Model Interfaces - Provides a standardized interface that decouples attack logic from specific neural network architectures.
Perturbation Constraint Mappings - Uses projection operations to ensure adversarial perturbations remain within valid image ranges.
Security Testing - Uses reference attack implementations to identify and fix security weaknesses in TensorFlow-based networks.
Adversarial Optimization Loops - Provides iterative optimization loops to refine adversarial noise within a defined perturbation budget.
Adversarial Attack Benchmarks - Provides reference implementations of known attacks to ensure reproducible robustness measurements across models.
Adversarial Security Tools - Library for benchmarking machine learning system vulnerability to adversarial examples.
Computer Vision Libraries - Library for adversarial example attacks and defenses.

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Que fait tensorflow/cleverhans ?

Cleverhans

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Cleverhans

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