Our advanced AI model, achieving 99% accuracy, is designed to detect deepfake audio, offering a robust defense against synthetic voice recordings and misinformation. Built with deep learning and integrated into a user-friendly Flask web app, this project allows users to upload audio files and instantly determine if they are real or fake. Ideal for final-year engineering students, this project combines cutting-edge AI techniques with real-world applications, making it perfect for IEEE publication.
Utilizes datasets like ASVspoof 2019, DFDC, and UrbanSound8K, with augmentations such as time-stretching, pitch-shifting, and noise injection to improve model generalization.
Combines MFCC and Mel spectrograms with Zero-Crossing Rate, Spectral Centroid, and Chroma features to capture detailed audio dynamics.
Employs CNN layers for local pattern detection, Bidirectional LSTMs for temporal dependencies, and an Attention Mechanism to focus on critical audio segments.
Combines Xception, CNN-BiLSTM, and Random Forest classifiers using Stacking and Majority Voting for enhanced prediction accuracy.
A user-friendly web app allows instant audio uploads and real-time classification, accessible to all users.
Incorporates Grad-CAM and SHAP to visualize key audio segments influencing the model’s decisions.
Uses AdamW optimizer, Cyclic Learning Rate, Cosine Annealing, 5-fold Cross-Validation, Grid Search, and Early Stopping to ensure optimal performance and prevent overfitting.
Optimized classification thresholds to balance precision and recall, minimizing false positives and negatives.
Detects manipulated voice recordings in news, podcasts, or public speeches.
Ensures voice recordings are genuine for legal or security purposes.
Assists content creators in verifying audio integrity during collaborations.
When you purchase this project, you gain access to a complete, end-to-end solution designed to ensure your success. Here's what we offer:
Receive fully functional and tested Python code, including the Flask web app, ready for implementation.
We assist in implementing the project on your system, ensuring smooth integration and providing full support throughout the process.
Get detailed documentation, including reports, PPTs, and raw data for research papers, ensuring a successful presentation and publication.
Benefit from ongoing mentorship and support, with assistance for any errors or improvements needed throughout your project journey.
This is one of the best IEEE project ideas for final-year students, combining deep learning, audio processing, and web development. We provide complete frontend and backend codes, along with detailed explanations to help you understand the project thoroughly. Our support extends to content for your report and IEEE paper publication.