TransferAttack is a pytorch framework to boost the adversarial transferability for image classification.
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Updated
May 28, 2024 - Python
TransferAttack is a pytorch framework to boost the adversarial transferability for image classification.
Simple code related to adversarial examples, attacks, and defenses.
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"
Repository of the TSFool method proposed in paper "TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack".
[MICCAI 2023] Official code repository of paper titled "Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation" accepted in MICCAI 2023 conference.
A curated collection of adversarial attack and defense on graph data.
[NeurIPS-2023] Annual Conference on Neural Information Processing Systems
SAGA: Spectral Adversarial Geometric Attack on 3D Meshes (ICCV 2023)
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models. [ICCV 2023 Oral]
An adversarial image generator
Repository of the Multi-TSFool method proposed in paper "TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack".
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks
Gaussian process regression-based adversarial image detection
Compose desired image with data such that will cause pretrained models misbehave.
Jeu de la bataille navale en Python avec simulation d'un joueur adverse
[CVPR 2021] Official repository for "Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing"
[SIGIR 2021] Official repository for "Targeted Attack and Defense for Deep Hashing"
Code to generate and extend the TCAB dataset.
Official implementation of CVPR2020 Paper "Cooling-Shrinking Attack"
[TMM 2022] Official repository for "Targeted Attack of Deep Hashing via Prototype-supervised Adversarial Networks"
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