Deep Fake Detection
This AI research prototype compares multiple CNN models for deepfake detection and contributed to a successful IEEE-published paper.
Advancing Deepfake Detection Through Research: A Custom AI System
At Haloxion, we collaborate closely with research teams to turn ambitious ideas into reliable, publication-ready systems. In this project, our client aimed to develop a deepfake detection framework strong enough to support a peer-reviewed research paper. The goal was to explore the effectiveness of multiple CNN architectures and produce measurable insights that could back an academic submission.
Understanding the Research Objective
The client needed more than a working model—they needed a credible, comparative study. Their requirements included:
- Evaluating multiple pre-trained architectures
- Running a consistent experimental pipeline
- Producing trustworthy metrics for publication
- Building a system capable of detecting deepfakes in both images and videos
- Compiling findings into a structured technical report
This meant our role extended beyond development into research design, experimentation, and documentation.
How We Approached the Solution
We structured the project around clarity, reproducibility, and scientific rigor:
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Model Comparison Approach
We implemented and evaluated three leading architectures—XceptionNet, InceptionV3, and MobileNetV2—allowing the client to compare performance across different CNN families.
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Unified Training & Evaluation Pipeline
A carefully prepared dataset, controlled preprocessing, and consistent hyperparameter tuning ensured that every model was evaluated under fair and repeatable conditions.
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Image & Video Inference System
We developed inference scripts that processed both images and video frames, giving the research real-world relevance.
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Technical Documentation for Publication
Beyond experimentation, we produced a complete technical report containing methodology, results, model reasoning, evaluation metrics, and academic-grade visuals.
The Final Outcome
The system successfully demonstrated the strengths and limitations of each pretrained architecture in deepfake detection. More importantly, the client achieved an impactful milestone—
the research work was accepted and published in an IEEE journal.
This project showcased Haloxion’s ability to support end-to-end research: from designing experiments to delivering insights strong enough for academic peer review.
Why It Matters
Deepfake detection remains a critical global challenge. Through this project, we helped contribute to the broader research community—supporting the development of systems that improve trust, safety, and digital integrity.
If you’re working on AI research, model experimentation, or technical documentation for publication, Haloxion can help turn your ideas into validated, high-quality results.