DeepTrust: Multimodal Deepfake Detection System
\nThis project implements a multi-modal digital forensics pipeline designed to detect hyper-realistic synthetic media by tracking concurrent spatial-temporal and acoustic anomalies. The architecture utilizes FFmpeg to extract synchronous, high-fidelity audio streams and frame sequences from video containers. The visual branch applies MTCNN for 5-point facial landmark alignment, feeding aligned frames into an AdaFace framework to extract quality-adaptive identity embeddings robust against compression. Concurrently, the audio branch isolates voice-cloning artifacts via Mel-spectrogram processing, projecting both feature spaces into a shared latent space where cross-modal transformers evaluate temporal coherence and cross-stream semantic alignment.
"}},{"node":{"frontmatter":{"title":"Real Time Predictive System & MLOps Pipeline","cover":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","placeholder":{"fallback":"data:image/png;base64,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"},"images":{"fallback":{"src":"/static/ae15c1f9b354bed82bf06ef3069ffc54/68e1b/image.png","srcSet":"/static/ae15c1f9b354bed82bf06ef3069ffc54/3ecea/image.png 200w,\n/static/ae15c1f9b354bed82bf06ef3069ffc54/4f34c/image.png 400w,\n/static/ae15c1f9b354bed82bf06ef3069ffc54/68e1b/image.png 800w,\n/static/ae15c1f9b354bed82bf06ef3069ffc54/4d3a0/image.png 1600w","sizes":"(min-width: 800px) 800px, 100vw"},"sources":[{"srcSet":"/static/ae15c1f9b354bed82bf06ef3069ffc54/69782/image.avif 200w,\n/static/ae15c1f9b354bed82bf06ef3069ffc54/fbdb9/image.avif 400w,\n/static/ae15c1f9b354bed82bf06ef3069ffc54/f850f/image.avif 800w,\n/static/ae15c1f9b354bed82bf06ef3069ffc54/9bc54/image.avif 1600w","type":"image/avif","sizes":"(min-width: 800px) 800px, 100vw"},{"srcSet":"/static/ae15c1f9b354bed82bf06ef3069ffc54/7e63c/image.webp 200w,\n/static/ae15c1f9b354bed82bf06ef3069ffc54/3fcc2/image.webp 400w,\n/static/ae15c1f9b354bed82bf06ef3069ffc54/a9868/image.webp 800w,\n/static/ae15c1f9b354bed82bf06ef3069ffc54/baa63/image.webp 1600w","type":"image/webp","sizes":"(min-width: 800px) 800px, 100vw"}]},"width":800,"height":380}}},"tech":["Python","MLOps","ETL Pipeline","Airflow","MLflow","DVC","Continuous Training","Feature Drift Detection"],"github":"https://github.com/NoorUlBaseer/Real-Time-Predictive-System-ETL-Pipeline.git","external":"","cta":null},"html":"Real-Time Market Sentiment Predictor
\nThis project implements a fully automated, continuous-lifecycle machine learning system engineered to ingest streaming financial text data and predict live asset market sentiment. The architecture features an automated data engineering pipeline that continuously scrapes financial news and social sentiments, orchestrating an end-to-end MLOps workflow from automated retraining to production deployment. By leveraging modular containerization and pipeline orchestration tools, the system tracks feature drift, handles automated model validation, and exposes low-latency inference endpoints to deliver real-time sentiment signals to downstream quantitative dashboards.
"}},{"node":{"frontmatter":{"title":"CI/CD Driven Kubernetes Deployment of MERN-App","cover":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","placeholder":{"fallback":"data:image/png;base64,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"},"images":{"fallback":{"src":"/static/bd601f18c53ec400549af95d07322a18/21284/image.png","srcSet":"/static/bd601f18c53ec400549af95d07322a18/a9c82/image.png 200w,\n/static/bd601f18c53ec400549af95d07322a18/58a3a/image.png 400w,\n/static/bd601f18c53ec400549af95d07322a18/21284/image.png 800w","sizes":"(min-width: 800px) 800px, 100vw"},"sources":[{"srcSet":"/static/bd601f18c53ec400549af95d07322a18/7d06a/image.avif 200w,\n/static/bd601f18c53ec400549af95d07322a18/c73f0/image.avif 400w,\n/static/bd601f18c53ec400549af95d07322a18/ecd63/image.avif 800w","type":"image/avif","sizes":"(min-width: 800px) 800px, 100vw"},{"srcSet":"/static/bd601f18c53ec400549af95d07322a18/b4f62/image.webp 200w,\n/static/bd601f18c53ec400549af95d07322a18/9609b/image.webp 400w,\n/static/bd601f18c53ec400549af95d07322a18/5d0e3/image.webp 800w","type":"image/webp","sizes":"(min-width: 800px) 800px, 100vw"}]},"width":800,"height":412}}},"tech":["MERN","JavaScript","Containerization","Docker","Kubernetes","CI/CD","GitHub Actions"],"github":null,"external":"https://github.com/NoorUlBaseer/CI-CD-Driven-Kubernetes-Deployment-of-MERN-App.git","cta":null},"html":"CI/CD Driven Kubernetes Deployment of MERN-App
\nThis project demonstrates the complete lifecycle of a modern full-stack e-commerce web application, built with the MERN stack. It covers everything from local development to a production-ready, containerized deployment process using Docker, with application orchestration and scaling handled by a local Kubernetes cluster (Minikube). Additionally, the project integrates GitHub Actions to automate continuous integration and continuous deployment (CI/CD), ensuring efficient, repeatable, and robust software delivery pipelines.
"}},{"node":{"frontmatter":{"title":"Face Mask Detection System","cover":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","placeholder":{"fallback":"data:image/png;base64,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"},"images":{"fallback":{"src":"/static/d7a9a31de5c17a4fa60b00f9822abf2a/dac2c/image.png","srcSet":"/static/d7a9a31de5c17a4fa60b00f9822abf2a/a20d6/image.png 200w,\n/static/d7a9a31de5c17a4fa60b00f9822abf2a/70e33/image.png 400w,\n/static/d7a9a31de5c17a4fa60b00f9822abf2a/dac2c/image.png 800w","sizes":"(min-width: 800px) 800px, 100vw"},"sources":[{"srcSet":"/static/d7a9a31de5c17a4fa60b00f9822abf2a/79784/image.avif 200w,\n/static/d7a9a31de5c17a4fa60b00f9822abf2a/7a702/image.avif 400w,\n/static/d7a9a31de5c17a4fa60b00f9822abf2a/77aac/image.avif 800w","type":"image/avif","sizes":"(min-width: 800px) 800px, 100vw"},{"srcSet":"/static/d7a9a31de5c17a4fa60b00f9822abf2a/d1768/image.webp 200w,\n/static/d7a9a31de5c17a4fa60b00f9822abf2a/da133/image.webp 400w,\n/static/d7a9a31de5c17a4fa60b00f9822abf2a/c0b17/image.webp 800w","type":"image/webp","sizes":"(min-width: 800px) 800px, 100vw"}]},"width":800,"height":522}}},"tech":["Python","AI","ML","DL","Computer Vision","PyTorch","OpenCV","NumPy","Pandas"],"github":null,"external":"https://github.com/NoorUlBaseer/Face-Mask-Detection-System.git","cta":""},"html":"\nThis project presents a custom deep learning solution for face mask detection, developed from scratch using PyTorch. It implements a lightweight Single Shot Detector (SSD) model to localize and classify facial regions in images based on whether a mask is worn or not. By intentionally avoiding pre-trained models, the system provides a hands-on, ground-up understanding of object detection architectures, custom loss functions, data preprocessing, and GPU-accelerated training workflows.
"}},{"node":{"frontmatter":{"title":"Mood-Based Music Recommender","cover":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","placeholder":{"fallback":"data:image/png;base64,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"},"images":{"fallback":{"src":"/static/25b321f6685fc01c355de3e3ad575cb0/aee42/image.png","srcSet":"/static/25b321f6685fc01c355de3e3ad575cb0/a6a8d/image.png 200w,\n/static/25b321f6685fc01c355de3e3ad575cb0/b6349/image.png 400w,\n/static/25b321f6685fc01c355de3e3ad575cb0/aee42/image.png 800w,\n/static/25b321f6685fc01c355de3e3ad575cb0/8c4e0/image.png 1600w","sizes":"(min-width: 800px) 800px, 100vw"},"sources":[{"srcSet":"/static/25b321f6685fc01c355de3e3ad575cb0/f0afd/image.avif 200w,\n/static/25b321f6685fc01c355de3e3ad575cb0/09bce/image.avif 400w,\n/static/25b321f6685fc01c355de3e3ad575cb0/40fd7/image.avif 800w,\n/static/25b321f6685fc01c355de3e3ad575cb0/e2825/image.avif 1600w","type":"image/avif","sizes":"(min-width: 800px) 800px, 100vw"},{"srcSet":"/static/25b321f6685fc01c355de3e3ad575cb0/6db13/image.webp 200w,\n/static/25b321f6685fc01c355de3e3ad575cb0/a7392/image.webp 400w,\n/static/25b321f6685fc01c355de3e3ad575cb0/2fefd/image.webp 800w,\n/static/25b321f6685fc01c355de3e3ad575cb0/bdd86/image.webp 1600w","type":"image/webp","sizes":"(min-width: 800px) 800px, 100vw"}]},"width":800,"height":429}}},"tech":["Python","Hugging Face API","Sentiment Analysis","Text Classification","Spotify Web API","RESTful APIs"],"github":null,"external":"https://mood-music-web-n339.onrender.com/","cta":"https://github.com/NoorUlBaseer/Mood-Based-Music-Recommender.git"},"html":"\nThis project implements an intelligent, context-aware audio curation system that dynamically generates personalized playlists based on user sentiment analysis. The pipeline utilizes the Hugging Face API to process raw text inputs through advanced Natural Language Processing models, extracting semantic undertones to classify the user's emotional state. These classified states are programmatically mapped to specific high-dimensional acoustic vectors, such as Valence, Energy, and Tempo, before executing multi-parameter queries against the Spotify Web API to retrieve and update target audio streaming queues in real time.
"}},{"node":{"frontmatter":{"title":"DevPulse API Manager","cover":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","placeholder":{"fallback":"data:image/png;base64,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"},"images":{"fallback":{"src":"/static/f8a4922a7160471890de6f0103fb9b38/3c4ce/image.png","srcSet":"/static/f8a4922a7160471890de6f0103fb9b38/5e981/image.png 200w,\n/static/f8a4922a7160471890de6f0103fb9b38/16db2/image.png 400w,\n/static/f8a4922a7160471890de6f0103fb9b38/3c4ce/image.png 800w,\n/static/f8a4922a7160471890de6f0103fb9b38/00ff2/image.png 1600w","sizes":"(min-width: 800px) 800px, 100vw"},"sources":[{"srcSet":"/static/f8a4922a7160471890de6f0103fb9b38/1d640/image.avif 200w,\n/static/f8a4922a7160471890de6f0103fb9b38/6357a/image.avif 400w,\n/static/f8a4922a7160471890de6f0103fb9b38/8e935/image.avif 800w,\n/static/f8a4922a7160471890de6f0103fb9b38/842f6/image.avif 1600w","type":"image/avif","sizes":"(min-width: 800px) 800px, 100vw"},{"srcSet":"/static/f8a4922a7160471890de6f0103fb9b38/26254/image.webp 200w,\n/static/f8a4922a7160471890de6f0103fb9b38/6b932/image.webp 400w,\n/static/f8a4922a7160471890de6f0103fb9b38/d6712/image.webp 800w,\n/static/f8a4922a7160471890de6f0103fb9b38/7233c/image.webp 1600w","type":"image/webp","sizes":"(min-width: 800px) 800px, 100vw"}]},"width":800,"height":426}}},"tech":["HTML","Tailwind CSS","Vanilla JavaScript","Web Storage State Persistence","Document Object Model (DOM)"],"github":"https://github.com/NoorUlBaseer/DevPulse-API-Manager.git","external":"https://dev-pulse-api-manager.vercel.app/","cta":null},"html":"\nThis project implements a serverless, client-side administrative console and API management dashboard designed to simulate enterprise SaaS application behavior entirely within the browser. The platform handles state persistence, user lifecycle verification, and real-time metric auditing completely on the frontend, eliminating the need for a server-side runtime or external database engines. By binding custom vector geometry with local state configurations, it provides an interactive console for API product auditing, programmatic data visualization, and role-based directory management .
"}},{"node":{"frontmatter":{"title":"Pakistani Cities Shortest Route Finder","cover":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","placeholder":{"fallback":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAKCAYAAAC0VX7mAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAA+0lEQVQoz5WRa27DIBCEff9jtVJvkT9pZUd+YMeAgd2FqSBqEicoVVYa7YKYj0E0KSXcKxcRYRwV5nkpUmoumqYZWpty5tGXZaxFg7v6A2bzx+cXjscf9P2Atjuh605o2w7DMO2Aj76mlvAyx+p+Ti8iTymvwFpCZsa6GoTAYJbS89paB+ZY9vaX/wMkZpyNgWeCo6wA613pee2J3gN6IXRmxJkNVtmwir11ttCyIb6VMAQMfQ/yHq+q+im1A5sLaEeN1SUsmzxJu/xhxbFPOKk6MHDCtyK0C2PQEb2W0i9zxGQi4hV48812qwPLrVGAFF89eAcLRAiHA34BDFERbMKWTpoAAAAASUVORK5CYII="},"images":{"fallback":{"src":"/static/07c2d64277c6213e7b12b853f3303c3e/aefd1/image.png","srcSet":"/static/07c2d64277c6213e7b12b853f3303c3e/107ed/image.png 200w,\n/static/07c2d64277c6213e7b12b853f3303c3e/9a6f9/image.png 400w,\n/static/07c2d64277c6213e7b12b853f3303c3e/aefd1/image.png 800w,\n/static/07c2d64277c6213e7b12b853f3303c3e/34d05/image.png 1600w","sizes":"(min-width: 800px) 800px, 100vw"},"sources":[{"srcSet":"/static/07c2d64277c6213e7b12b853f3303c3e/af9f4/image.avif 200w,\n/static/07c2d64277c6213e7b12b853f3303c3e/94a25/image.avif 400w,\n/static/07c2d64277c6213e7b12b853f3303c3e/471cd/image.avif 800w,\n/static/07c2d64277c6213e7b12b853f3303c3e/491e4/image.avif 1600w","type":"image/avif","sizes":"(min-width: 800px) 800px, 100vw"},{"srcSet":"/static/07c2d64277c6213e7b12b853f3303c3e/19d43/image.webp 200w,\n/static/07c2d64277c6213e7b12b853f3303c3e/f9846/image.webp 400w,\n/static/07c2d64277c6213e7b12b853f3303c3e/d9e33/image.webp 800w,\n/static/07c2d64277c6213e7b12b853f3303c3e/ba3c6/image.webp 1600w","type":"image/webp","sizes":"(min-width: 800px) 800px, 100vw"}]},"width":800,"height":419}}},"tech":["Python","Dijkstra's Algorithm","Graph Theory","NetworkX Matplotlib","Adjacency Lists","Min-Priority Queues"],"github":null,"external":"https://pakistani-cities-shortest-route-finder.streamlit.app/","cta":"https://github.com/NoorUlBaseer/Pakistani-Cities-Shortest-Route-Finder"},"html":"Pakistani Cities Shortest Route Finder
\nThis project implements a graph-based spatial optimization application designed to compute and visualize the most efficient pathways across the major road networks of Pakistan. By modeling geographical cities as nodes and interconnecting motorways or national highways as weighted edges, the system applies Dijkstra's Algorithm paired with min-priority queues for low-latency pathfinding. Built entirely from scratch without high-level routing APIs, it provides a practical framework for spatial data structure design, edge-relaxation methodologies, and dynamic topological graph visualization using NetworkX.
"}}]}}}