In this project, students will implement and test a number of state-of-the-art synthetic audio tools (including open-source ML/AI models) to explore the realm of possibilities in audio detection.
Deepfake technologies, which allow malicious actors to produce fake images, videos, and audio clips, are reaching an unprecedented convergence of quality, scalability, and ease of use. It will soon be possible to mass-produce highly realistic synthetic content that may be generated and spread faster than fake media detectors can manage. The proliferation of these technologies poses clear threats to society and democracy (for example, consider the dangers of shared videos wherein politicians give fake speeches). It appears that the future of information channels which we rely on when forming our beliefs and opinions is on the shaky ground, unless detection technology can gain the upper hand. Synthetic audio detection is one key element in managing this threat.
The project will culminate in a real-world exercise in which audio clips published in the recent Anthony Bourdain documentary "Roadrunner" - clips which the filmmakers claim to be indiscernible as real or fake - will be tested by the students' custom toolkit. Students will generate:
|Length of commitment||Longer than a semester (about 6-9 months )|
|Level of collaboration||Small group project (2-3 students) |
|Engagement format||Hybrid Project|
|Benefits||Research experience; academic credit; possible financial compensation|
|Who is eligible ||Sophmores and juniors with experince in programming (Python); machine learning; experience in classes at UB CSE preferred |
Once you begin the digital badge series, you will have access to all the necessary activities and instructions. Your mentor has indicated they would like you to also complete the specific preparation activities below. Please reference this when you get to Step 2 of the Preparation Phase.