Audio Deepfake Detection through Self Supervised Learning
Budget: $250 – $750 USD
This project's primary objective is to identify fake audio recordings with utmost precision. As a deep learning expert, your role will be to apply advanced techniques to ensure accurate detection and classification of deepfake audios.
Highlight of Roles:
- Design and implement deepfake detection algorithms to identify manipulated audio.
- Ensure the algorithms developed are highly precise in identifying authentic from artificial audios.
Desired Skills:
- Proficiency in Convolutional Neural Networks (CNN).
- Experience in working with audio data and detection algorithms.
- Ability to create high precision detection systems.
- Familiarity with deep learning frameworks.
The relevant datasets and model code have already been identified, this will be a matter of tranining the models on the specified datasets and integrating with a flask API such that the model produces a binary classification of whether an incoming audio recording is a deepfake or not. Please familizarize yourself with the PSPD document attached below to gain a better understanding
Highlight of Roles:
- Design and implement deepfake detection algorithms to identify manipulated audio.
- Ensure the algorithms developed are highly precise in identifying authentic from artificial audios.
Desired Skills:
- Proficiency in Convolutional Neural Networks (CNN).
- Experience in working with audio data and detection algorithms.
- Ability to create high precision detection systems.
- Familiarity with deep learning frameworks.
The relevant datasets and model code have already been identified, this will be a matter of tranining the models on the specified datasets and integrating with a flask API such that the model produces a binary classification of whether an incoming audio recording is a deepfake or not. Please familizarize yourself with the PSPD document attached below to gain a better understanding