Interval Bound Propagation

Interval Bound Propagation (IBP) is a method for verifying the robustness of neural networks, primarily focusing on determining the impact of input perturbations on the network's output. Current research emphasizes improving IBP's accuracy by addressing limitations like the "wrapping effect" and exploring alternative techniques such as affine arithmetic and mixed monotonicity methods, particularly for handling uncertainties in network weights and biases. This work is crucial for ensuring the safety and reliability of neural networks in critical applications like autonomous driving and medical diagnosis, where understanding and bounding the network's output range under uncertainty is paramount.