  Deep learning is applied to many complex tasks in the field of wireless
communication, such as modulation recognition of spectrum waveforms, because of
its convenience and efficiency. This leads to the problem of a malicious third
party using a deep learning model to easily recognize the modulation format of
the transmitted waveform. Some existing works address this problem directly
using the concept of adversarial examples in the image domain without fully
considering the characteristics of the waveform transmission in the physical
world. Therefore, we propose a low-intercept waveform~(LIW) generation method
that can reduce the probability of the modulation being recognized by a third
party without affecting the reliable communication of the friendly party. Our
LIW exhibits significant low-interception performance even in the physical
hardware experiment, decreasing the accuracy of the state of the art model to
approximately $15\%$ with small perturbations.
