The document outlines a summer internship project at Dhanalakashmi Srinivasan College focusing on developing an AI-based deepfake detection system using advanced machine learning techniques. The project aims to accurately identify manipulated media to combat misinformation, enhance digital content integrity, and support applications in cybersecurity and media verification. It details the methodology, including data collection, preprocessing, feature extraction, model training, and performance evaluation, achieving a test accuracy of around 90% in detecting deepfakes.

1. DHANALAKASHMI SRINIVASAN
COLLEGE OF ENGINEERING AND
TECHNOLOGY
ELECTRONICS AND COMMUNICATION ENGINEERING
EC3711 Summer Internship
AI BASED DEEPFAKE DETECTION SYSTEM
Name : Vignesh D
Reg.No :310521106072
Company Name : VEI TECHNOLOGIES

2. Organization Overview
Title - Artificial Intelligence.
Organization - VEI Technology of Private Limited.
Place -Mudichur , Chennai
Date - 15-07-2024 to 16-08-2024

3. • Deepfakes are synthetic media—whether in the form of videos, images, or audio
—created using deep learning techniques, particularly Generative Adversarial
Networks (GANs), to manipulate or fabricate content.
• These manipulated media often appear disturbingly realistic and can be used for
malicious purposes such as spreading misinformation, creating fake news, or
committing identity theft.
Project Overview

4. • The rise of deepfake technology has led to a significant challenge in verifying the
authenticity of digital content.
• In an age where media is consumed rapidly and frequently on social platforms,
distinguishing real from fake content is becoming more difficult.
• Traditional methods for detecting deepfakes are either inefficient, manual, or
prone to error.
• This makes it necessary to develop more accurate, scalable, and automated
solutions using AI and machine learning
Problem Statement

5. ❖ To design and implement an advanced deepfake detection system capable
of accurately identifying manipulated or synthetic media (images, videos,
or audio) using state-of-the-art machine learning techniques. The system
aims to enhance digital content integrity, mitigate misinformation, and
provide a reliable tool for applications in cybersecurity, media verification,
and law enforcement.
Objective of the Project

6. • Deepfakes have become a potent tool for creating and spreading
misinformation, which can have far-reaching consequences in society,
especially in areas like politics, elections, and public discourse.
• News Integrity
• Trust in Digital
Project Significance

7. • The methodology for developing an AI-Based Deepfake Detection System is
divided into several key stages:
• data collection
• preprocessing
• feature extraction
• model development
• training and
• evaluation
Methodology for AI-Based Deepfake Detection System

8. Objective: Gather datasets containing both real and deepfake media
(images, videos, audio).
• Tools/Technologies:
1. Public datasets like DeepFake Detection Challenge (DFDC),
FaceForensics++, Google’s DeepFake Dataset, and CelebA (for
facial images).
2. Web scraping tools
Data Collection and Dataset Selection

9. • Objective: Clean and prepare data for model input. This includes face
detection and alignment, audio preprocessing, and splitting the data into
training and testing sets.
• Tools/Technologies:
1. OpenCV and dlib for face detection
2. Librosa and PyDub for audio
3. NumPy and Pandas
Data Preprocessing

10. • Objective: Extract relevant features from both visual and audio data that
can be used by machine learning models for classification.
• Tools/Technologies:
1. Convolutional Neural Networks (CNNs)
2. Pre-trained models like VGG16, ResNet50
3. Spectrogram analysis using Librosa to extract audio.
Feature Extraction

11. • Objective: Train deep learning models to detect deepfakes by leveraging
extracted features from video and audio.
• Tools/Technologies:
1. Deep Learning Frameworks
2. Convolutional Neural Networks (CNNs
3. Recurrent Neural Networks (RNNs) or LSTMs
4. Transfer Learning
Model Selection and Training

12. • Objective: Evaluate the performance of the trained model using standard
metrics such as accuracy, precision, recall, and F1-score.
• Tools/Technologies:
1. Scikit-learn
2. TensorBoard
Model Evaluation

13. Objective: Deploy the trained model for real-time media analysis via a user-
friendly interface.
Tools/Technologies:
• Flask/Django
• TensorFlow.js
• Cloud Platforms
Real-Time Deployment and Application

14. Gather deepfake datasets
Data preprocessing
Feature extraction
Model Selection &
Training
Model Evaluation
Real-time
deployment
Data collection
Face detection, audio processing
Visual features (CNN),Audio feature(MFCCs)
CNNs for images,RNN/LSTM for audio/vedio
Accuracy , Precision , Recall , F1 Score
Web interface/API for real time use
FLOWCHART:

15. OUTPUT

16. • Detection Accuracy
• Model Performance: Achieved a test accuracy of around 90% for
identifying deepfakes.
• Precision, Recall, F1-Score: High precision (e.g., 92%) and recall (e.g.,
88%), with an F1-score of approximately 90%, indicating a balanced and
effective classification performance.
• Comparison with Baselines: If you tried multiple models, e.g., CNN vs.
transfer learning models (e.g., ResNet), compare their accuracies, showing
any improvement.
Results & Key Outcomes

17. • Inference Speed: Provide the average time taken for the model to detect
deepfakes per image or per second of video, indicating if it’s feasible for
real-time or batch processing.
• Memory/Processing Requirements: Indicate if the model is lightweight
enough for deployment on common hardware (e.g., mobile, cloud).
Computational Efficiency

18. • This project successfully developed an AI-based deepfake detection
system with promising accuracy, showcasing the potential of deep
learning to counter the challenges posed by deepfakes.
• The model performed well on standard datasets, effectively
distinguishing between real and fake media, but encountered some
limitations with high-quality deepfakes.
Conclusion

19. • Enhanced Model Architectures: Implement advanced models like
transfer learning or hybrid techniques to improve accuracy and robustness.
• Real-time Detection: Optimize the model for real-time deployment in
applications like social media monitoring or video conferencing.
• Cross-Domain Testing: Expand testing to diverse datasets and deepfake
types, ensuring the model generalizes well across different sources and
environments.
Future Scope

20. Thank you for
your time