Mr wang's starred repositories
yolov5_adversarial
Generate adversarial patches against YOLOv5 🚀
ACM-IEEE-arXiv-Spider
Crawl information of papers from ACM/IEEE/arXiv/AAAI digital library.
stealth.min.js
Automatically generate the newest stealth.min.js.
PromptPapers
Must-read papers on prompt-based tuning for pre-trained language models.
Prompt-Engineering-Guide
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
open-parse
Improved file parsing for LLM’s
chatgpt-arxiv-extension
A browser extension that enhance search engines with ChatGPT
Awesome-LLM-in-Social-Science
Awesome papers involving LLMs in Social Science.
LLM-Agent-Paper-List
The paper list of the 86-page paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.
AgentVerse
🤖 AgentVerse 🪐 is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation
TransferAttack
TransferAttack is a pytorch framework to boost the adversarial transferability for image classification.
robust-physical-attack
Physical adversarial attack for fooling the Faster R-CNN object detector
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.
Traffic_Sign_Recognition
Use yolov5 for traffic sign detection
Traffic-Sign-Detection-with-RetinaNet
利用RetinaNet实现交通标志检测