Multi-Architecture Deepfake Detection System
A comparative deepfake detection case study using CGAN and DCGAN to reveal how synthetic media is generated and how manipulated images can be identified.
Building a Multi-Architecture Deepfake Detection System
Deepfakes are becoming increasingly realistic, making it difficult for individuals, institutions, and even automated systems to distinguish between genuine and manipulated media. Our goal for this project was to explore how different generative architectures behave, and more importantly, how they can help us build a system capable of identifying fake content before it spreads.
Rather than focusing on one model, we designed an experimental setup using two complementary architectures—CGAN and DCGAN—to understand how synthetic media is created and how it can be detected more reliably.
Understanding the Challenge
Deepfakes today pose risks ranging from misinformation to identity misuse. The client wanted a system that could:
- Demonstrate how fake images are produced
- Compare different generation techniques
- Build a discriminator capable of detecting manipulated visuals
- Communicate the dangers of deepfakes through a clear, visual workflow
The project was not just about training models—it was about unpacking the mechanics behind deepfake creation and detection in a way that’s explainable and accessible.
Our Approach
1. Comparing Two Generative Routes
We used CGANs to see how conditioning (like labels or attributes) affects image manipulation, and DCGANs to study how deepfake-like images emerge purely from noise and learned patterns. This allowed us to understand how targeted vs. untargeted manipulations are created in real scenarios.
2. Training Paired Generators and Discriminators
Both architectures were trained with their respective discriminators. This helped us observe not only how fake images evolve over training, but also how discriminators learn to pick up visual inconsistencies—mirroring how deepfake-detection models operate.
3. Building a Practical Detection Insight
By analyzing discrepancies across both systems, we could highlight the artifacts, distortions, and inconsistencies that real detection systems rely on—giving the client a grounded understanding of how deepfake detectors are built and how they fail.
4. Delivering Clear, Visual Evidence
Instead of a purely technical outcome, we provided:
- Side-by-side visual outputs from CGAN and DCGAN
- Trained models for experimentation
- A simple workflow to reproduce the detection process
- Insights on how real-world deepfake detection pipelines operate
The goal was to make deepfake mechanics tangible through examples rather than theory.
Impact
The final deliverable served as an educational and analytical tool, helping the client understand:
- How synthetic faces are generated
- Why different GAN structures produce different types of fakes
- How discriminators learn to detect manipulation
- What practical challenges exist in deepfake detection
This project provided clarity on a rapidly evolving threat landscape and gave the client a foundation to explore advanced detection systems in the future.