本科毕业设计：针对Deepfake假脸视频面部细节特征的提取算法

In this repository you will find everything you need to know about Convolutional Neural Network, and how to implement the most famous CNN architectures in both Keras and PyTorch. (I'm working on implementing those Architectures using MxNet and Caffe)

An advanced web application that detects deepfake content in uploaded videos using AI-powered analysis with real-time monitoring and diagnostics.

IT4343E | Computer Vision Project

A sleek PyTorch‑powered DeepFake detection pipeline that fuses the state‑of‑the‑art ResNeXt‑50 CNN backbone with an LSTM‑based temporal encoder. Rigorous evaluation on DFDC and AvLips datasets yields 95.19% and 98.88% test accuracy, respectively.

Skin cancer detection chatbot for a Facebook Page using computer vision and deep learning

Exploring the Application of Attention Mechanisms in Conjunction with Baseline Models on the COVID-19-CT Dataset

ResUnet and Unet with resNeXt-50 backbone implementation for semantic segmentation

This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features.

TrustLens is an AI-based deepfake detection system that combines spatial, temporal, and semantic analysis using ResNeXt-50, Bidirectional LSTM, and SigLIP classifier. Built as a production-ready full-stack web application with Docker deployment, interactive artifact visualization, and explainable confidence scores.

Preprocessed face-only videos with the face_recognition library and implemented a deepfake detection model using the Resnext-50 architecture. Achieved accurate identification of manipulated videos through advanced computer vision techniques.