DeepRobust

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DeepRobust is an AI Frameworks & Infra tool that provides a PyTorch adversarial library for attack and defense methods on images and graphs. It helps researchers and engineers develop and test robust machine learning models.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is DeepRobust?

DeepRobust is a comprehensive PyTorch adversarial library designed for both attack and defense methods across image and graph domains. It offers a robust toolkit for researchers and engineers to develop and evaluate the resilience of machine learning models against adversarial attacks. The library includes various algorithms for generating adversarial examples and implementing defense strategies, with continuous updates adding new attacks like UGBA for backdoor attacks on graphs and PRBCD for scalable graph attacks. DeepRobust also supports robust models like AirGNN and provides tools for converting datasets between PyTorch Geometric and DeepRobust, making it a versatile platform for adversarial machine learning research.

Best used for

Ideal for developers and data scientists who need to implement and test adversarial attacks on image and graph-based machine learning models, and develop robust defense mechanisms. Especially valuable for researchers exploring the security and resilience of AI systems against malicious inputs.

Common actions

develop adversarial attacks

implement defense mechanisms

evaluate model robustness

research machine learning security

workflowsopen-source"AI Agents"collaborationface swappingautomated workflowlow-code/no-codegithub copilotdeepfake

Capabilities

Key features

Image adversarial attack generation
Graph adversarial attack generation
Defense model training
Dataset conversion tools
Scalable graph attack algorithms
Black box image attacks

Target Audience

developerdata scientistprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of adversarial attacks does DeepRobust support?

DeepRobust supports a wide range of adversarial attacks on both images and graphs. For images, it includes methods like PGD and various black-box attacks. For graphs, it features attacks such as Metattack, UGBA (backdoor attacks), and PRBCD (scalable attacks), allowing for comprehensive testing of model vulnerabilities.

Can DeepRobust be used to train robust machine learning models?

Yes, DeepRobust provides functionalities for training defense models against adversarial attacks. For instance, it includes PGDtraining for image models and supports robust graph models like AirGNN. This allows users to develop and evaluate models that are more resilient to adversarial perturbations.

What are the primary dependencies for using DeepRobust?

DeepRobust primarily requires Python 3.6 or higher and PyTorch 1.2.0 or higher. It can be installed via pip or from the source code. The library also provides an option to install without dependency packages, which can be useful for managing complex environments.

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