Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
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Updated
Jun 27, 2024 - Python
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Finding Property Violations through Network Falsification: Challenges, Adaptations and Lessons Learned from OpenPilot
[UAI 2024 paper] DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution.
用于检测图像中不良内容的深度学习模型,对输入图像进行暴力和非暴力的二分类,并通过AIGC图像、对抗样本和加噪图像进行了增强。
Revisiting Transferable Adversarial Images (arXiv)
[CVPR2024 Highlight] Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning
alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)
RSS feed for adversarial example papers.
a Pytorch library for security research on speaker recognition, released in "Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition" accepted by TDSC
An unofficial version of the PyTorch implementation of CURE and Fast Adversarial training with FGSM.
Library containing PyTorch implementations of various adversarial attacks and resources
🛡 A set of adversarial attacks in PyTorch
A pytorch adversarial library for attack and defense methods on images and graphs
auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
Code for the ICCV 2021 paper "Augmented Lagrangian Adversarial Attacks"
Official code for "PubDef: Defending Against Transfer Attacks From Public Models" (ICLR 2024)
An approach to curating naturally adversarial datasets.
[CVPR 2024] Boosting Adversarial Transferability by Block Shuffle and Rotation
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