Simple code related to adversarial examples, attacks, and defenses.
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Updated
Mar 28, 2024 - Jupyter Notebook
Simple code related to adversarial examples, attacks, and defenses.
Neural Network Adversarial Attack Method Based on Improved Genetic Algorithm
[MICCAI 2023] Official code repository of paper titled "Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation" accepted in MICCAI 2023 conference.
Jeu de la bataille navale en Python avec simulation d'un joueur adverse
Repository of the Multi-TSFool method proposed in paper "TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack".
adversarial attack and defense tests
Compose desired image with data such that will cause pretrained models misbehave.
Code to generate and extend the TCAB dataset.
This github repository contains the official code for the papers, "Robustness Assessment for Adversarial Machine Learning: Problems, Solutions and a Survey of Current Neural Networks and Defenses" and "One Pixel Attack for Fooling Deep Neural Networks"
Gaussian process regression-based adversarial image detection
A collection of adversarial attacks on various models built using Deep Learning and Deep Metric Learning techniques. Standard datasets are used.
GraphReach : Position-Aware Graph Neural Network using Reachability Estimations, IJCAI'21
[TMM 2022] Official repository for "Targeted Attack of Deep Hashing via Prototype-supervised Adversarial Networks"
SAGA: Spectral Adversarial Geometric Attack on 3D Meshes (ICCV 2023)
Repository of the TSFool method proposed in paper "TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack".
An adversarial image generator
[SIGIR 2021] Official repository for "Targeted Attack and Defense for Deep Hashing"
vanilla training and adversarial training in PyTorch
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models. [ICCV 2023 Oral]
From Gradient Leakage to Adversarial Attacks in Federated Learning
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