vignesh ppt-1 is a ppt for DDS hardware and software
- 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. Project Overview: • The AI-Based Deepfake Detection System aims to address the growing problem of fake media in the digital age. • 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. • The goal of this project is to build an AI-powered system that can detect deepfakes in images, videos, and audio recordings, providing an automated solution to this growing problem.
- 3. • Problem Statement: • 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
- 4. • Objective of the Project: • Accurate Detection of Deepfakes: Develop a robust AI model capable of distinguishing between genuine and fake media across multiple modalities, including images, videos, and audio. • Real-Time Analysis: Ensure the system can analyze media content in real- time, making it suitable for integration into applications that require immediate results, such as social media platforms, news organizations, or cybersecurity tools. • User-Friendly Interface: Build a simple and intuitive user interface that allows anyone—whether a researcher, journalist, or casual user—to upload media and quickly receive an analysis of whether it has been manipulated. • High Detection Performance: Achieve high accuracy, precision, recall, and F1-score to ensure the detection system is both reliable and effective, minimizing false positives and false
- 5. • Project Significance : • 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: The project helps news agencies, journalists, and media houses detect deepfakes, ensuring that the content they share with the public is authentic and accurate. • Trust in Digital Content: By providing an effective detection system, this project contributes to preserving trust in digital content, safeguarding the credibility of online media platforms.
- 6. • Methodology for AI-Based Deepfake Detection System : • The methodology for developing an AI-Based Deepfake Detection System is divided into several key stages: 1. data collection 2. preprocessing 3. feature extraction 4. model development 5. training and 6. evaluation
- 7. • Data Collection and Dataset Selection • Objective: Gather datasets containing both real and deepfake media (images, videos, audio). • Tools/Technologies: – Public datasets like DeepFake Detection Challenge (DFDC), FaceForensics++, Google’s DeepFake Dataset, and CelebA (for facial images). – Web scraping tools (if custom datasets are needed). • Process: Collect and curate a mix of real and deepfake media samples (images, videos, and audio files) to ensure the model is trained on diverse data representing multiple types of deepfake manipulations.
- 8. • Data Preprocessing • 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: – OpenCV and dlib for face detection and alignment (detecting and extracting faces from video frames). – Librosa and PyDub for audio preprocessing (extracting features such as pitch, tone, and cadence from audio files). – NumPy and Pandas for handling and processing data structures (arrays, data frames). • Process: • For Video: – Extract frames from video files. – Detect and align faces using OpenCV and dlib. – Normalize the frames to a fixed size and format (e.g., 224x224 pixels for CNNs).
- 9. • Feature Extraction • Objective: Extract relevant features from both visual and audio data that can be used by machine learning models for classification. • Tools/Technologies: – Convolutional Neural Networks (CNNs) for visual feature extraction. – Pre-trained models like VGG16, ResNet50 (for CNN-based feature extraction from images). – Spectrogram analysis using Librosa to extract audio features (e.g., MFCCs, spectral roll-off, zero-crossing rate). • Process: • For Video: Use CNNs to extract visual features from frames and faces in videos (e.g., facial features, anomalies in expressions).
- 10. • Model Selection and Training • Objective: Train deep learning models to detect deepfakes by leveraging extracted features from video and audio. • Tools/Technologies: – Deep Learning Frameworks: TensorFlow, Keras, and PyTorch. – Convolutional Neural Networks (CNNs) for image-based (frame) deepfake detection. – Recurrent Neural Networks (RNNs) or LSTMs (Long Short-Term Memory) for temporal (video/audio) analysis of deepfake inconsistencies. – Transfer Learning: Using pre-trained models like ResNet, InceptionV3, or VGG16 to speed up the training process. • Process: • Visual Models: Train CNNs on extracted frames or face images to classify real vs. fake content.
- 11. • Model Evaluation • Objective: Evaluate the performance of the trained model using standard metrics such as accuracy, precision, recall, and F1-score. • Tools/Technologies: – Scikit-learn for calculating performance metrics like accuracy, precision, recall, F1-score, and confusion matrix. – TensorBoard for visualizing training metrics and loss curves. • Process: • Use the testing set to evaluate the model’s accuracy in detecting deepfakes. • Generate confusion matrix and classification reports to analyze false positives and false negatives.
- 12. Real-Time Deployment and Application: Objective: Deploy the trained model for real-time media analysis via a user- friendly interface. Tools/Technologies: Flask/Django (for web-based deployment and API development). TensorFlow.js (for integrating deep learning models directly into web applications). Cloud Platforms: AWS, Google Cloud, or Microsoft Azure for hosting and scaling the solution. Process: Create a web interface or an API where users can upload media files (videos/images/audio) for deepfake detection. Provide a confidence score and visual feedback on detected inconsistencies.
- 13. FLOWCHART: +-------------------------+ | Data Collection | ← Gather deepfake datasets +-------------------------+ | v +-------------------------+ | Data Preprocessing | ← Face detection, audio processing +-------------------------+ | v +-------------------------+ | Feature Extraction | ← Visual features (CNN), Audio features (MFCCs) +-------------------------+ | v +-------------------------+ | Model Selection & | ← CNN for images, RNN/LSTM for audio/video | Training | +-------------------------+ | v +-------------------------+ | Model Evaluation | ← Accuracy, Precision, Recall, F1-Score +-------------------------+ | v +-------------------------+ | Real-Time Deployment | ← Web interface/API for real-time use +-------------------------+
- 14. • Results & Key Outcomes • 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.
- 15. Computational Efficiency • 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).
- 16. Conclusion • 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. • Overall, the project highlights the importance of continuous advancement in deepfake detection to safeguard against misinformation and privacy risks.
- 17. • Future Scope • 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.