01. The Challenge
Rising deepfake content threatens media authenticity and trust.
02. The Solution
Built CNN-based detection system with multi-criteria decision making for robust deepfake identification.
Overview
With the rise of realistic AI-generated media, distinguishing truth from fiction is a critical challenge. This system uses a custom CNN architecture focused on detecting artifacts common in GAN-generated faces.
Multi-Criteria Decision Making (MCDM)
We didn't rely on a single model. We used an ensemble approach where MCDM algorithms (like TOPSIS) weighed the outputs of different detectors (eye blinking, lip sync, artifact analysis) to make a final decision.
System Architecture
Custom CNN architecture
MCDM for decision fusion
TensorFlow/Keras implementation
OpenCV for preprocessing
Project Links
Technologies
TensorFlowKerasCNNOpenCVPython
Key Impact
95% detection accuracy
Real-time processing
MCDM-enhanced reliability