FLOB: Protecting Floating-Point Computation in DNN Binaries with MBA Obfuscation
Researchers have introduced FLOB, an obfuscation framework designed to protect floating-point computations in deep neural network (DNN) binaries using Mixed Boolean-Arithmetic (MBA) transformations. FLOB lifts floating-point values into a higher-precision binary expansion space, performs computations there, and projects results back to the original precision without introducing additional rounding error. Experiments demonstrate that FLOB reduces the operator recovery rate to 4.51%, representing a 32.82 percentage-point improvement over existing obfuscation methods.
Why it matters: This work offers a practical and effective method for safeguarding proprietary DNN models against reverse engineering, specifically addressing the unique challenges of floating-point operations.
Full story at: arXiv Cryptography and Security ↗