Synthetic Image Detection

Role: Machine Learning Engineer at Magnet Forensics
Tools: Python, Machine Learning, Weights & Biases, Image scraping, AWS

At Magnet Forensics, I worked with the AI team to develop a system that detects AI-generated (synthetic) images in digital investigations. Deepfakes and other fake images were becoming more common, and existing tools couldn’t reliably identify them.

We built a machine learning model trained on scraped image data from various sources. The system was designed to help investigators quickly flag suspicious content and reduce manual review time. We used Weights & Biases to track and validate model performance.

This project made synthetic image detection faster, more reliable, and easier to integrate into existing forensic workflows.

Video Signature Matching

Role: Machine Learning Engineer at Griffeye Technologies
Tools: C++, Python, Docker, Azure

At Griffeye Technologies, I was part of a team that developed and implemented an algorithm called Video Signature Matching. Investigators often work with vast amounts of video content, and manually identifying overlapping or repeated scenes across cases can be challenging and time-consuming.

The tool enables users to automatically find visually similar scenes across large video datasets, making it easier to uncover duplicates, spot patterns, and support case linkage within forensic workflows.

Media Types Classifier

Role: Software Developer at Griffeye Technologies
Tools: Python, Machine Learning, Docker, Azure

At Griffeye Technologies, I worked on a machine learning tool called Media Types Classifier. In digital investigations, investigators are often overwhelmed by large volumes of irrelevant images — such as drawings, logos, icons, or screenshots from games.

This tool was designed to automatically classify and filter out such media types, allowing investigators to focus on more relevant content. We trained and evaluated models capable of identifying and excluding unimportant or non-actionable image categories from the review workflow, helping streamline investigations and reduce noise.

Airline Crew Rostering

This is the second use case description, emphasizing how it streamlines a key process and enhances the overall experience for users.

Tools: Functional programming, behave, python, jenkins