Deepfake Detection Using Deep Learning

Exposing Deepfakes with AI: Securing Digital Truth.

Comment

🔍 Deepfake Detection: Exposing Digital Manipulation 🚀 Inspiration With the rise of deepfake technology, distinguishing real from fake has become increasingly difficult. Deepfakes pose serious risks in misinformation, identity theft, and cybersecurity. Inspired by the need for robust detection systems, we built an AI-powered deepfake detection model to safeguard digital authenticity.

🛠 How We Built It Our deepfake detection system leverages MobileNetV2 for image classification and an autoencoder for anomaly detection. Here's a step-by-step breakdown:

Dataset Preparation

We used the real_and_fake_face dataset, containing real and deepfake images. Images were preprocessed with grayscale conversion, resizing, and normalization. Model Training

Autoencoder: Trained to learn real face patterns and detect anomalies in fake faces. MobileNetV2: Fine-tuned on labeled deepfake images to classify real vs. fake. Feature Enhancements

Grad-CAM Visualization: Highlights manipulated areas in images. Blur Detection: Identifies artifacts caused by deepfake generation. Facial Landmark Analysis: Detects inconsistencies in facial structures. Deployment & UI

Gradio Interface: Built an interactive web app for image and video detection. Google Colab Integration: Ensured easy access to training and evaluation. 💡 What We Learned The importance of data preprocessing for improving model accuracy. How autoencoders can detect anomalies in an unsupervised manner. Challenges in training deep learning models with imbalanced datasets. The effectiveness of explainable AI (Grad-CAM) in model interpretability. ⚠️ Challenges Faced Data Quality Issues: Many deepfake images had varying resolutions and noise. Computational Limitations: Training deep networks required high GPU power. Video Analysis Complexity: Processing frame-by-frame deepfake detection efficiently.

Built With

Share this project:

Updates