Pinned Repositories
Adversarial-Attack-on-Fine-Tuned-Flood-Detection-Model
Implementation of FGSM (Fast Gradient Sign Method) attack on fine-tuned MobileNet architecture trained for flood detection in images.
CLIPping-the-Deception
Code and pre-trained models for our paper "CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection".
Efficient-Named-Entity-Recognizer-using-Structured-Perceptron-Viterbi-and-Beam-Search-Python
This script employs Dynamic Programming algorithms such as, Viterbi Search and Beam Search, to accelerate the process of training Multilayer Perceptron
Flood-Detection-from-Images-using-Deep-Learning
Flood detection from images using deep learning. Deep learning library KERAS was employed and MobileNet architecture was fine-tuned for image classification task.
Named-Entity-Recognition-using-Structured-Perceptron-Python
Named Entity Recogniser using Structured Perceptron in Python
Phishing-Websites-Classification-using-Deep-Learning
A detailed comparison of performance scores achieved by Machine Learning and Deep Learning algorithms on 3 different Phishing datasets. 3 different feature selection and 2 different dimensionality reduction techniques are used for comparison.
Searching-for-exotic-particles-in-high-energy-physics-using-classic-supervised-learning-algorithms
Supervised classification algorithms employed to explore and identify Higgs bosons from particle collisions, like the ones produced in the Large Hadron Collider. HIGGS dataset is used..
Sentence-Completion-using-Hidden-Markov-Models
The goal of this script is to implement three langauge models to perform sentence completion, i.e. given a sentence with a missing word to choose the correct one from a list of candidate words. The way to use a language model for this problem is to consider a possible candidate word for the sentence at a time and then ask the language model which version of the sentence is the most probable one.
Simple-SQL-XSS-Fuzzing-Tool-PYTHON-
Video-Transformer-for-Deepfake-Detection
This is the PyTorch implementation of our ACM Multimedia 2021 paper titled "Video Transformer for Deepfake Detection with Incremental Learning".
sohailahmedkhan's Repositories
sohailahmedkhan/CLIPping-the-Deception
Code and pre-trained models for our paper "CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection".
sohailahmedkhan/Flood-Detection-from-Images-using-Deep-Learning
Flood detection from images using deep learning. Deep learning library KERAS was employed and MobileNet architecture was fine-tuned for image classification task.
sohailahmedkhan/Phishing-Websites-Classification-using-Deep-Learning
A detailed comparison of performance scores achieved by Machine Learning and Deep Learning algorithms on 3 different Phishing datasets. 3 different feature selection and 2 different dimensionality reduction techniques are used for comparison.
sohailahmedkhan/Sentence-Completion-using-Hidden-Markov-Models
The goal of this script is to implement three langauge models to perform sentence completion, i.e. given a sentence with a missing word to choose the correct one from a list of candidate words. The way to use a language model for this problem is to consider a possible candidate word for the sentence at a time and then ask the language model which version of the sentence is the most probable one.
sohailahmedkhan/Video-Transformer-for-Deepfake-Detection
This is the PyTorch implementation of our ACM Multimedia 2021 paper titled "Video Transformer for Deepfake Detection with Incremental Learning".
sohailahmedkhan/Adversarial-Attack-on-Fine-Tuned-Flood-Detection-Model
Implementation of FGSM (Fast Gradient Sign Method) attack on fine-tuned MobileNet architecture trained for flood detection in images.
sohailahmedkhan/Named-Entity-Recognition-using-Structured-Perceptron-Python
Named Entity Recogniser using Structured Perceptron in Python
sohailahmedkhan/Searching-for-exotic-particles-in-high-energy-physics-using-classic-supervised-learning-algorithms
Supervised classification algorithms employed to explore and identify Higgs bosons from particle collisions, like the ones produced in the Large Hadron Collider. HIGGS dataset is used..
sohailahmedkhan/Simple-SQL-XSS-Fuzzing-Tool-PYTHON-
sohailahmedkhan/Deepfake-Detection
Deepfake Detection: Analysing Model Generalisation Across Architectures, Datasets, and Pre-Training Paradigms
sohailahmedkhan/Efficient-Named-Entity-Recognizer-using-Structured-Perceptron-Viterbi-and-Beam-Search-Python
This script employs Dynamic Programming algorithms such as, Viterbi Search and Beam Search, to accelerate the process of training Multilayer Perceptron
sohailahmedkhan/Best-README-Template
An awesome README template to jumpstart your projects!
sohailahmedkhan/Custom-Video-Dataset-in-PyTorch
Notes about usage: This code is to create a custom video dataset to train deeplearning models using PyTorch on consecutive video frames extracted from a video. This code expects the extracted video frames in separate folders. For example, video1's frames will be in a folder named 'video1'. You can use OpenCV to extract video frames and save inside folders.
sohailahmedkhan/detecting-cheapfakes-code
sohailahmedkhan/keras
Deep Learning for humans
sohailahmedkhan/Language-Modeling-using-PyTorch
A very simple Neural Language Model in PyTorch
sohailahmedkhan/LRNet
Landmark Recurrent Network: An efficient and robust framework for Deepfakes detection
sohailahmedkhan/OpenCV-Face-Extraction
OpenCV face detection and extraction from videos.
sohailahmedkhan/pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
sohailahmedkhan/resume
Personal Website
sohailahmedkhan/ScalableML
COM6012 Scalable Machine Learning - University of Sheffield
sohailahmedkhan/sohailahmedkhan.github.io
My Resume
sohailahmedkhan/stable-diffusion-webui
Stable Diffusion web UI
sohailahmedkhan/Transformers
This repository contains resources relating to Transformer models including theoretical concepts and code examples.