Defending Against Adversarial Attacks One Layer at a Time
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Updated
Oct 26, 2021 - Jupyter Notebook
Defending Against Adversarial Attacks One Layer at a Time
Simple code related to adversarial examples, attacks, and defenses.
This repository implements the Invgan defense architecture in Pytorch. InvGAN acts as an initialisation for DefenseGAN and can help in State of the Art robustness of target models against adversarial attacks.
Evaluating the Use of Fast Adversarial Training in Defending Against Adversarial Patch Attacks
Adversarial Patch defense using SegmentAndComplete (SAC) & Masked AutoEncoder (MAE)
A modified model for self-driving car that is resilient to adversarial attacks
Adversarial defense by retreaval-based methods
some examples for adversarial attack and defense with pytorch
Adversarial Defense using Generative Adversarial Networks
Improving model's robustness to transfer attacks by regularizing projection of input gradients.
An efficient framework for establishing baselines in standard and adversarial machine learning training projects
Source code for ESORICS 2020 paper "Detection by attack: Detecting adversarial samples by undercover attack"
LittleAdversary is an adversarial machine learning library made to aid research into adversarial attacks and defences, with a primary focus on one-shot defences. It contains an end-to-end implementation of the proposed defence in 'Siamese Neural Networks for Adversarial Robustness ', complete with statistical analysis of the results.
Tensors-based framework for adversarial robustness
Robust Object Detection Fusion Against Deception
6th place solution to KDD CUP 2020 Graph Adversarial Attacks & Defense
Augmentation for CV using frequency shortcuts
Official webpage for the paper 'Defending against Audio Adversarial Examples on Speaker Recognition Systems'.
Consensus Adversarial Defense Method Based on Augmented Examples
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