Pytorch-Named-Entity-Recognition-with-transformers

Implementation of gradient-based adversarial attack(FGSM,MI-FGSM,PGD)

A Parallel Graphlet Decomposition Library for Large Graphs

Implementation of adversarial training under fast-gradient sign method (FGSM), projected gradient descent (PGD) and CW using Wide-ResNet-28-10 on cifar-10. Sample code is re-usable despite changing the model or dataset.

Reproduce multiple adversarial attack methods

Paddle-Adversarial-Toolbox (PAT) is a Python library for Deep Learning Security based on PaddlePaddle.

Official implementation of the paper: "REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions", IEEE WACV, 2022

Structural Wing Model Reduction in Fluid-Structure Interactions

some adversarial attacks implemented on different ml models to see their effect.

Code implementation of our AAAI'22 paper "Improved Gradient-Based Adversarial Attacks for Quantized Networks"

some tricks for sentence representation

Projected Gradient Descent (PGD), inverted and amplified -> prompt & generate images with CLIP

对抗测试

Programa de Gestão e Desempenho (PGD) versão SESEP (Secretaria de Avaliação e Gestão da Informação) em Docker/container.

Defence of Five

A cross-layer IoMT anomaly detection dataset with real traffic, labeled attacks, and FGSM/PGD adversarial samples for robust security research.

"A collection of deep learning projects from my PGD in Data Science at NED University, including CNN-based image classification and sequence processing with ConvNets, applied to datasets like Cat-Dog, Netflix, and Jena Climate."

A comprehensive GUI application for visualizing and understanding adversarial attacks on deep neural networks.

PyTorch implementation of projected gradient descent (PGD) adversarial noise attack

Open-source CLI tool for adversarial data watermarking — renders audio, text, tabular, image, and video data unusable for AI training and RAG pipelines while preserving human fidelity. PGD attacks against CLIP/Whisper, homoglyph injection, semantic shifting, statistical poisoning.