A list of recent papers about adversarial learning

A novel physical adversarial attack tackling the Digital-to-Physical Visual Inconsistency problem.

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

Adversary Emulation Framework

Adversarial attacks on LLMs, for influencing outputs of hidden layer linear probes and steering generations.

Beacon Object File (BOF) launcher - library for executing BOF files in C/C++/Zig applications

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convol…

Hybrid neural network model is protected against adversarial attacks using either adversarial training or randomization defense techniques

A classical or convolutional neural network model with adversarial defense protection

The Fast Gradient Sign Method (FGSM) combines a white box approach with a misclassification goal. It tricks a neural network model into making wrong predictions. We use this technique to anonymize images.

A reading list for large models safety, security, and privacy.

The Security Automation Toolkit

Generate adversarial patches against YOLOv5 🚀

In the dynamic landscape of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks like PGD adversarial attack.

WACV 2024 Papers: Discover cutting-edge research from WACV 2024, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ support visual intelligence development!

Learning-Based Atk: a black box adversarial attack method for TSF.

A Python API that facilitates training, creating, and transferring attacks with quantized DNNs

Attack to induce LLMs within hallucinations

[ICML 2024] Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

A suite for hunting suspicious targets, expose domains and phishing discovery