Skip to main content

A PyTorch library for adversarial attacks, inspired by torchattacks.

Project description

AdvTorchAttacks

AdvTorchAttacks is a PyTorch-based library for generating adversarial attacks on deep learning models. It provides implementations of widely used attack methods, enabling user to evaluate model robustness against adversarial perturbations.

Features

  • Implements FGSM, PGD, CW, MIFGSM, and AutoAttack adversarial attack methods.
  • Works with PyTorch models.
  • Returns adversarial images, perturbations images.

Installation

You can install advtorchattacks in two ways:

1️ Install from PYPI

pip install advtorchattacks

2 Install from GitHub

https://github.com/santhosh1705kumar/advtorchattacks

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

advtorchattacks-2.0.tar.gz (7.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

advtorchattacks-2.0-py3-none-any.whl (9.8 kB view details)

Uploaded Python 3

File details

Details for the file advtorchattacks-2.0.tar.gz.

File metadata

  • Download URL: advtorchattacks-2.0.tar.gz
  • Upload date:
  • Size: 7.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.4

File hashes

Hashes for advtorchattacks-2.0.tar.gz
Algorithm Hash digest
SHA256 b2c86ebd6c496b629a4829ec5586010dcd8c3337a76bd0d7f158b9dbcffc3c7b
MD5 f79e41795493767b2342ad73a8ae58ee
BLAKE2b-256 1c0c74eeeda1148882121a39773c7a2fe063bd0488e01c80f63738ba2145935d

See more details on using hashes here.

File details

Details for the file advtorchattacks-2.0-py3-none-any.whl.

File metadata

  • Download URL: advtorchattacks-2.0-py3-none-any.whl
  • Upload date:
  • Size: 9.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.4

File hashes

Hashes for advtorchattacks-2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 6440b603dd38a7709d277f970418d3a7ac85e49d5543617a4bd7383aca64f4e5
MD5 99980af9b43171dcba841f6803b2a372
BLAKE2b-256 9d98d46e751ae0b5130b54c182a8b47525bfdbadefd392a83d47121df3ec1655

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page