👀🛡️ Code for the paper “Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2024)
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Updated
May 24, 2024 - Python
👀🛡️ Code for the paper “Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2024)
Machine Learning Attack Series
[ICML 2024] Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models
Code for the paper "Multi-scale Diffusion Denoised Smoothing" (NeurIPS 2023)
auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs
Must-read Papers on Textual Adversarial Attack and Defense
Simple code related to adversarial examples, attacks, and defenses.
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Adversarial defense by retreaval-based methods
Adversarial Attack and Defense in Deep Ranking, T-PAMI, 2024
Implementation of the paper "Improving the Accuracy-Robustness Trade-off of Classifiers via Adaptive Smoothing".
[Pattern Recognition 2024] Towards Robust Neural Networks via Orthogonal Diversity"
A modified model for self-driving car that is resilient to adversarial attacks
Feature Separation and Recalibration (CVPR 2023 Highlights)
An efficient framework for establishing baselines in standard and adversarial machine learning training projects
[ICLR 2021] "InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective" by Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan, Ruoxi Jia, Bo Li, Jingjing Liu
Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.
Official PyTorch implementation of "Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks" (AAAI 2022)
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Tensors-based framework for adversarial robustness
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