L1n111ya

mask-grounding

Awesome-CLIP

Awesome list for research on CLIP (Contrastive Language-Image Pre-Training).

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.

filebrowser

📂 Web File Browser

smoothing

Provable adversarial robustness at ImageNet scale

Certified-Watermarking

Adversarial-Example-Attack-and-Defense

This repository contains the implementation of three adversarial example attack methods FGSM, IFGSM, MI-FGSM and one Distillation as defense against all attacks using MNIST dataset.

CS-Notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计

MIA_project

Update when I learning membership inference attack.

Blind-MIA

This is code, for "Practical Blind Membership Inference Attack via Differential Comparison" , which is I learnt yet.

openCV_learning

SL-MIA

Membership inference attack is all swarm-learning need