Deepfake Detection For Images Using CNN

 

1. Introduction

Deepfake technology—powered by AI and Generative Adversarial Networks (GANs)—has brought forward both creative innovation and serious ethical concerns. These synthetic images are almost indistinguishable from authentic photos, raising challenges for security, journalism, digital forensics, and media integrity. This project tackles the urgent need for reliable deepfake detection, utilising Convolutional Neural Networks (CNNs) to identify subtle manipulations in images.

2. Project Overview

This project aims to develop a robust, scalable, and accurate system to detect deepfake images. By leveraging the Real and Fake Face Detection dataset and state-of-the-art deep learning techniques, the system achieves 91.67% accuracy in identifying manipulated images. The core of the system is a CNN-based architecture trained to distinguish fine-grained image artifacts often missed by the human eye.

3. Key Features

• 🔍 CNN-Based Feature Extraction: Automatically captures intricate textures, edges, and spatial patterns.

• ⚙️ End-to-End Model Pipeline: Includes data preprocessing, training, evaluation, and deployment.

• 📉 Performance Metrics: Evaluated with accuracy, precision, recall, F1-score, and confusion matrix.

• 🚀 Real-Time Deployment: Model containerized using Docker and deployable via AWS, GCP, or edge devices.

• 🎯 High Accuracy: Achieves 91.67% classification accuracy.

4. Technologies and Models Used

• Core Algorithms: Convolutional Neural Networks (CNN), Binary Cross-Entropy loss

• Libraries & Tools: TensorFlow, Keras, OpenCV, Dlib

• Deployment Stack: Docker, AWS EC2/SageMaker, GCP AI Platform

• Hardware: NVIDIA GeForce RTX 3080 for accelerated training

• Dataset: Real and Fake Face Detection Dataset (Kaggle)

5. How It Works

➤ Data Preprocessing

• Images resized to 224×224 pixels and normalized

• Augmentation via flipping, rotation, and color adjustment

• Data split: 80% training, 10% validation, 10% testing

➤ Model Architecture

• Multiple convolution and max-pooling layers

• Batch normalization for improved training stability

• Dropout to reduce overfitting

• Fully connected (FC) layers followed by a Softmax classifier

  

  Architecture of CNN

➤ Training Strategy

• Binary Cross-Entropy loss with Adam optimizer

• Hyperparameter tuning (batch size = 32, epochs = 300)

• Early stopping to avoid overfitting

➤ Deployment

• Packaged as a REST API

• Scalable via cloud or edge device deployment

6. Applications

• Media & Journalism: Detect falsified images before publication

• Law Enforcement: Authenticate forensic evidence

• Social Media Platforms: Flag misleading content automatically

• Digital Forensics: Ensure the integrity of archived visuals

• AI Safety: Prevent misuse of generative models

7. Sample Results

Results

Model Prediction

8. Conclusion

As GANs continue to produce more convincing fakes, the need for intelligent detection grows. This CNN-powered approach delivers strong accuracy and adaptability for real-world deployment. However, to stay ahead in the ongoing "arms race" with deepfake creators, future directions may include:

• Multi-modal detection (e.g., combining audio and image data)

• Adversarial training

• Federated learning

• Edge-optimized models for real-time analysis

About the Author 

Natuva Bhavana

BTech in Computer Science and Engineering 

Alliance University

Email: bhavanaviswanath2@gmail.com

Presented at:
ETTIS-2025 (5th International Conference on Emerging Trends and Technologies on Intelligent Systems)
Organized by: CDAC, in collaboration with Springer, UPG Romania, Université de Haute-Alsace, and Northeastern University (USA)

Final Thoughts

This project marks a significant milestone in my academic journey, blending AI research, practical implementation, and global presentation. If you're passionate about AI safety, deep learning, or digital forensics, I encourage you to explore deepfake detection—it's a field where every innovation matters.