  Neural networks have increasingly influenced people's lives. Ensuring the
faithful deployment of neural networks as designed by their model owners is
crucial, as they may be susceptible to various malicious or unintentional
modifications, such as backdooring and poisoning attacks. Fragile model
watermarks aim to prevent unexpected tampering that could lead DNN models to
make incorrect decisions. They ensure the detection of any tampering with the
model as sensitively as possible.However, prior watermarking methods suffered
from inefficient sample generation and insufficient sensitivity, limiting their
practical applicability. Our approach employs a sample-pairing technique,
placing the model boundaries between pairs of samples, while simultaneously
maximizing logits. This ensures that the model's decision results of sensitive
samples change as much as possible and the Top-1 labels easily alter regardless
of the direction it moves.
