About
Provides specialized guidance for reverse-engineering the internal parameters of two-layer ReLU neural networks through black-box query access. By leveraging the piecewise linear structure of ReLU functions, this skill facilitates the recovery of weight matrices up to permutation and scaling through techniques like critical point detection, Hessian analysis, and Independent Component Analysis (ICA). It ensures high-fidelity model extraction by emphasizing unique row matching and one-to-one correspondence over simple functional fitting, making it an essential resource for security researchers and developers performing model extraction attacks or neural network reverse-engineering.
