Detecting Deepfakes Using Machine Learning on Audio 

In this talk, I will go over deepfake audios and the impact it has on society. The continuous advancements made in deepfake technology have led to many unprecedented privacy and security issues, including false information generated quickly and cheaply using generative AI (ChatGPT). I will be going over my project which focuses on detecting deepfake audio through lexical content using machine learning.

Sneha Muppalla is currently studying a dual degree in Electrical Engineering and Computer Science (EECS) and Business under UC Berkeley’s MET Program. Her technical interests focus on computer vision and building generative vision systems that go beyond pattern recognition and towards physical and causal understanding. She is especially interested in the intersection of visual intelligence, generative models, and real-world perception, with experience in CS coursework, systems, and research exploration.