InferNet: Exploiting Aggregate GPU Profiles as Side-Channel for DNN Architecture Inference

Understand

Deep Neural Networks (DNNs) have become ubiquitous for their ability to solve problems across various domains, including computer vision, natural language processing, and speech recognition.

• However, as their adoption grows, they face a range of security threats, such as model stealing, architecture extraction, and manipulation, which can compromise their integrity, privacy, and functionality.
• Past works have relied on complex, fine-grained, and time-series analysis to launch DNN model extraction attacks.
• These approaches require extensive amounts of data, which are often challenging to acquire and analyze effectively.