cserezwan

MLAlgorithms

Minimal and clean examples of machine learning algorithms implementations

MLAlgorithms

Minimal and clean examples of machine learning algorithms implementations

Perturbation-Inactivate

Adversarial Defense, Adversarial Attack, Face Recognition, Learnable PCA, Pytorch

MorDIFF

MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders

adv-patch-paper-list

A paper list for localized adversarial patch research

SFace-Privacy-friendly-and-Accurate-Face-Recognition-using-Synthetic-Data

SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data

CR-FIQA

Official repository for CVPR2023 paper, CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

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.

Face-Recognition

Face Recognition using pre-trained model built-on Arcface was implemented on Pytorch.

adversarial-attacks-pytorch

A pytorch implementations of Adversarial attacks and utils

AdversarialMask

Original PyTorch implementation of the AdversarialMask paper

QMagFace

QMagFace: Simple and Accurate Quality-Aware Face Recognition (WACV 2023)

yolov7

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

GFPGAN

GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

ElasticFace

Official repository of CVPRW2022 paper, ElasticFace: Elastic Margin Loss for Deep Face Recognition

frame-interpolation

FILM: Frame Interpolation for Large Motion, In ECCV 2022.

grad-cam-pytorch

PyTorch re-implementation of Grad-CAM (+ vanilla/guided backpropagation, deconvnet, and occlusion sensitivity maps)

ML-Course-Notes

🎓 Sharing machine learning course / lecture notes.

face-occlusion-generation

[CVPRW 2022] Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets

FD_Com

Comparison of FD systems

Face

I have published my face related codes in this repository

Proctoring-AI

Creating a software for automatic monitoring in online proctoring

face-detection-algorithms-comparison

Face detection algorithms

adversarial-attack-on-mtcnn

adversarial patch train by I-FGSM to attack on MTCNN face detection system

University-of-Kent-PhD-Thesis-Template

Unofficial University of Kent PhD Thesis Template

Thesis-Template

This is a thesis template for PhD students at University of Kent

FaceAdv

A physical-world attack for face recognition systems

DualAttentionAttack

FaceAttack

yolov5-face

YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931) ECCV Workshops 2022)