Cybersecurity

In the world of machine learning, especially with the rise of large language models (LLMs) and deep neural networks, model extraction attacks are a growing concern. These attacks aim to replicate the behavior of a machine learning model by querying it and then using the responses to reverse-engineer its underlying architecture and parameters. What is a Model Extraction Attack? A model extraction attack occurs when an adversary tries to replicate a machine learning model by making repeated queries to it and analyzing its responses. The goal of the attacker is to create a new model that mimics the target model’s functionality, often without direct access to its architecture or parameters. ...

Speaker anonymization refers to the process of modifying the characteristics of a speaker’s voice so that the speaker’s identity cannot be easily determined while preserving the speech’s intelligibility. With the increasing usage of speech data in virtual assistants, surveillance systems, and other applications, ensuring privacy in speech data has become critical. In this post, we’ll dive into the technical details of speaker anonymization techniques, including implementation approaches using machine learning, deep learning models, and popular libraries. ...