Creation of a deepfake audio detection system using machine learning to distinguish between authentic and manipulated audio recordings, aiming to combat the spread of misinformation and cyber fraudulent.
This project aims to develop a robust deepfake audio detection system capable of identifying manipulated audio content generated using artificial intelligence (AI) techniques. Deepfake audio poses a significant threat as it can be used to spread misinformation, impersonate individuals, and manipulate public opinion. By leveraging machine learning algorithms and audio processing techniques, our goal is to create a reliable solution for detecting deepfake audio and differentiating it from authentic recordings.
Implement preprocessing techniques to enhance the quality of audio samples and extract relevant features for analysis.
Utilize convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning architectures to classify audio samples as real or fake.
Evaluate the performance of the detection system using metrics such as accuracy, precision, recall, and F1 score.
Integrate the trained model into existing audio verification systems or develop standalone applications for real-time deepfake detection. User Interface: Design a user-friendly interface with visualization tools for analyzing detection results and facilitating user interaction.
Compatible with major operating systems like Windows, macOS, and Linux.
Python 3.6 or higher is required for implementing the deepfake audio detection system.
TensorFlow and PyTorch are essential for training deep learning models and conducting audio analysis tasks.
Librosa and PyDub are necessary for efficient audio processing, feature extraction, and manipulation.
Scikit-learn for implementing machine learning algorithms and evaluation metrics.
Git for version control management and collaborative development.
Use of popular Integrated Development Environments (IDEs) such as VSCode, PyCharm, or Jupyter Notebook for coding and debugging.
Includes Pandas, NumPy, Matplotlib for data manipulation, visualization, and analysis tasks. Additionally, libraries like SoundFile and SpeechRecognition may be required for specific audio processing tasks.
The Deepfake Audio Detection System represents a critical advancement in combating the proliferation of manipulated audio content. By leveraging cutting-edge machine learning algorithms and signal processing techniques, it offers a robust solution for distinguishing between authentic and deepfake-generated audio. With applications across various domains including journalism, entertainment, and online platforms, the system plays a pivotal role in upholding the integrity of digital media. Its implementation fosters trust and accountability in digital communication channels, thereby mitigating the dissemination of false information and preserving the credibility of audio-based content. Through proactive detection and mitigation of deepfake threats, the system contributes to maintaining the authenticity and reliability of audio content in the digital age.
- Warke, K., Dalavi, N., Nahar, S., Gurule, D., Jadhav, U., & Prachi. (Year). Deepfake Detection through Deep Learning using ResNeXt CNN and LSTM. International Journal of Artificial Intelligence and Applications, Volume(X), Issue(X), Pages(X-X).
- Lim, S.-Y., Chae, D.-K., & Lee, S.-C. (Year). Detecting Deepfake Voice Using Explainable Deep Learning Techniques. Journal of Artificial Intelligence Research, Volume(X), Issue(X), Pages(X-X).
- Amerini, I., Galteri, L., Caldelli, R., & Del Bimbo, A. (Year). Deepfake Video Detection through Optical Flow based CNN. Journal of Computer Vision and Image Understanding, Volume(X), Issue(X), Pages(X-X).