Code with experiments from paper "Continual learning for computer security"
-
Updated
May 10, 2023
Code with experiments from paper "Continual learning for computer security"
The official implementation of the ICONIP2021 paper: Condition-Invariant Physical Adversarial Attacks via Pixel-Wise Adversarial Learning
This study explores the vulnerability of the Federated Learning (FL) model where a portion of clients participating in the FL process is under the control of adversaries who don’t have access to the training data but can access the training model and its parameters.
Evaluating the Use of Fast Adversarial Training in Defending Against Adversarial Patch Attacks
Fast Gradient Sign Adversarial Attack(FGSM) examples creation using FashionMnist dataset
A deep-learning tool for detecting adversarial attacks on French text classifiers.
Replicating the code and results of the paper "Simple Black-box Adversarial Attacks"
Neural Structured Learning (Adversarial) with TensorFlow
Adversarial attack demo for the "Cybersecurity for data science" course.
A modified model for self-driving car that is resilient to adversarial attacks
Implementation of adversarial training on CIFAR-10 dataset.
Your go to spot for creating and using Jespipe plugins.
This work demonstrates an altogether different utility of attention heads. Self-attention heads are characteristic of Transformer models and have been well studied for interpretability and pruning, but here we build a novel adversarial detection model based on them.
Analyzing the Effect of Adversarial Inputs on Saliency Maps
To evaluate the performance of each regularization method (cutout, mixup, and self-supervised rotation predictor), we apply it to the CIFAR-10 dataset using a deep residual network with a depth of 20 (ResNet20)
Add a description, image, and links to the adversarial-attacks topic page so that developers can more easily learn about it.
To associate your repository with the adversarial-attacks topic, visit your repo's landing page and select "manage topics."