Benchmarking Local Robustness of High-Accuracy Binary Neural Networks
  for Enhanced Traffic Sign Recognition
Andreea Postovan Mădălina Eraşcu
Traffic signs play a critical role in road safety and traffic management for
autonomous driving systems. Accurate traffic sign classification is essential
but challenging due to real-world complexities like adversarial examples and
occlusions. To address these issues, binary neural networks offer promise in
constructing classifiers suitable for resource-constrained devices.
  In our previous work, we proposed high-accuracy BNN models for traffic sign
recognition, focusing on compact size for limited computation and energy
resources. To evaluate their local robustness, this paper introduces a set of
benchmark problems featuring layers that challenge state-of-the-art
verification tools. These layers include binarized convolutions, max pooling,
batch normalization, fully connected. The difficulty of the verification
problem is given by the high number of network parameters (905k - 1.7 M), of
the input dimension (2.7k-12k), and of the number of regions (43) as well by
the fact that the neural networks are not sparse.
  The proposed BNN models and local robustness properties can be checked at
https://github.com/ChristopherBrix/vnncomp2023_benchmarks/tree/main/benchmarks/traffic_signs_recognition.
  The results of the 4th International Verification of Neural Networks
Competition (VNN-COMP'23) revealed the fact that 4, out of 7, solvers can
handle many of our benchmarks randomly selected (minimum is 6, maximum is 36,
out of 45). Surprisingly, tools output also wrong results or missing
counterexample (ranging from 1 to 4). Currently, our focus lies in exploring
the possibility of achieving a greater count of solved instances by extending
the allotted time (previously set at 8 minutes). Furthermore, we are intrigued
by the reasons behind the erroneous outcomes provided by the tools for certain
benchmarks.