tingsongpku's Stars
tingsongpku/tingsongpku.github.io
Trusted-AI/adversarial-robustness-toolbox
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
BDBC-KG-NLP/QA-Survey-CN
北京航空航天大学大数据高精尖中心自然语言处理研究团队开展了智能问答的研究与应用总结。包括基于知识图谱的问答(KBQA),基于文本的问答系统(TextQA),基于表格的问答系统(TableQA)、基于视觉的问答系统(VisualQA)和机器阅读理解(MRC)等,每类任务分别对学术界和工业界进行了相关总结。
microsoft/unadversarial
Official repository for our NeurIPS 2021 paper "Unadversarial Examples: Designing Objects for Robust Vision"
vision4robotics/Ad2Attack
This is the official code for the paper "Ad2Attack: Adaptive Adversarial Attack for Real-Time UAV Tracking".
idrl-lab/Adversarial-Attacks-on-Object-Detectors-Paperlist
A Paperlist of Adversarial Attack on Object Detection
ZJU-OpenKS/OpenKS
OpenKS - 领域可泛化的知识学习与计算平台
idrl-lab/A-Paperlist-of-Adversarial-Attack-on-3D-Object-Detection
nowangry/Paperlist-for-Adversarial-Attack-on-3D-Object-Detection
idrl-lab/PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
idrl-lab/Full-coverage-camouflage-adversarial-attack
https://idrl-lab.github.io/Full-coverage-camouflage-adversarial-attack/
idrl-lab/idrlnet
IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.
microsoft/AI-System
System for AI Education Resource.
liuhuanyong/QASystemOnMedicalKG
A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建,自动问答,基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱,并以该知识图谱完成自动问答与分析服务。
shawnh2/QA-CivilAviationKG
基于民航业知识图谱的自动问答系统
baifengbai/aviation
构建航空领域知识图谱
git-disl/TOG
Real-time object detection is one of the key applications of deep neural networks (DNNs) for real-world mission-critical systems. While DNN-powered object detection systems celebrate many life-enriching opportunities, they also open doors for misuse and abuse. This project presents a suite of adversarial objectness gradient attacks, coined as TOG, which can cause the state-of-the-art deep object detection networks to suffer from untargeted random attacks or even targeted attacks with three types of specificity: (1) object-vanishing, (2) object-fabrication, and (3) object-mislabeling. Apart from tailoring an adversarial perturbation for each input image, we further demonstrate TOG as a universal attack, which trains a single adversarial perturbation that can be generalized to effectively craft an unseen input with a negligible attack time cost. Also, we apply TOG as an adversarial patch attack, a form of physical attacks, showing its ability to optimize a visually confined patch filled with malicious patterns, deceiving well-trained object detectors to misbehave purposefully.
shengyp/talks
ultralytics/yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
nlsde-safety-team/DualAttentionAttack
jakevdp/PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
iswbm/magic-python
Python 黑魔法手册
d2l-ai/d2l-zh
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
xiangchong1/3d-adv-pc
Generating 3D Adversarial Point Clouds
CyberMonitor/APT_CyberCriminal_Campagin_Collections
APT & CyberCriminal Campaign Collection
OpenCTI-Platform/opencti
Open Cyber Threat Intelligence Platform
kuangliu/pytorch-cifar
95.47% on CIFAR10 with PyTorch
mit-han-lab/pvcnn
[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning
Yochengliu/awesome-point-cloud-analysis
A list of papers and datasets about point cloud analysis (processing)
QingyongHu/SoTA-Point-Cloud
🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey