Audio DeepFake Detection using Machine Learning and Deep Learning | AI based Projects 2024-25
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🎥 AI-Powered Deepfake Audio Detection | Real vs Fake Audio Classifier 🎧🤖
Welcome to our latest video, where we showcase an advanced AI model built to detect deepfake audio with 99% accuracy! 🚀 Whether you're concerned about fake voice recordings or deepfakes spreading misinformation, this solution offers a powerful defense against synthetic audio. 💥
In this video, we walk you through the entire process, from data preparation to model deployment in a Flask web app. The AI model can analyze uploaded audio and determine if it’s real or fake in a matter of seconds! 🕒
🔍 What’s Inside?
Enhanced Data Preparation & Augmentation 🛠️
To ensure our model can handle a variety of audio formats and types, we used multiple datasets, including ASVspoof 2019, DFDC, and UrbanSound8K. We augmented the audio by applying time-stretching, pitch-shifting, and noise injection to boost the model's ability to generalize across different audio manipulations. 🎶
Advanced Feature Extraction 🎚️
We combined the power of MFCC (Mel-frequency cepstral coefficients) with Mel spectrograms to capture perceptual frequency characteristics and detailed temporal information. Additional spectral features like Zero-Crossing Rate (ZCR), Spectral Centroid, and Chroma features further enhanced the model’s understanding of audio dynamics. 🎛️
CNN-BiLSTM Hybrid with Attention 💥
Our custom-built deep learning model uses CNN layers to capture local audio patterns and Bidirectional LSTMs to analyze long-range temporal dependencies in the audio sequence. The Attention Mechanism allows the model to focus on the most important parts of the audio, improving its ability to detect subtle differences between real and fake audio. 🧠💡
Ensemble Learning 🔗
To ensure the model is as robust as possible, we combined the outputs of different classifiers (like Xception, CNN-BiLSTM, and Random Forest) using Stacking and Majority Voting techniques. This ensemble approach helps the model make more accurate predictions. 🏆
Web Application 🌐
We didn’t stop at building the model—we made it user-friendly! 🎉 The model has been integrated into a simple and intuitive Flask web app, allowing anyone to upload an audio file and instantly see if it’s real or fake. Whether you’re working on media authentication or just curious about deepfakes, this tool is accessible for all! 📱
📊 Key Features:
99% Accuracy: After fine-tuning and extensive validation, our model consistently delivers high accuracy.
Real-Time Classification: Upload an audio file and get instant feedback on whether it’s fake or real.
Explainable AI (XAI): We incorporated Grad-CAM and SHAP to visualize which parts of the audio contribute to the final decision, offering deeper insights into how the model makes its decisions. 🔍
Post-Processing: Optimized classification thresholds for better precision and recall, balancing false positives and negatives.
🔧 Optimization & Fine-Tuning:
AdamW Optimizer: Used for better generalization and reduced overfitting.
Cyclic Learning Rate and Cosine Annealing schedules to improve model convergence.
5-fold Cross-Validation and Grid Search for hyperparameter tuning to ensure optimal performance.
Early Stopping: To save the best-performing model and prevent overfitting.
💡 Real-World Applications:
Media Authentication: Detecting manipulated voice recordings or deepfake audio in news, podcasts, or public speeches. 📡
Security & Forensics: Ensuring voice recordings are genuine for legal or security purposes. 🔐
Content Creation: Assisting content creators to verify audio integrity when collaborating with others. 🎙️
🚀 What’s Next?
We’re planning to expand this AI model to detect video deepfakes as well, making it a comprehensive multimedia deepfake detection tool! Stay tuned for more updates, and don’t forget to subscribe for future content! 🔔
Like, Comment, and Subscribe if you find this project fascinating and want to see more cutting-edge AI solutions! 👍💬
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