  In recent years, much work have studied the use of convolutional neural
networks for gravitational-wave detection. However little work pay attention to
whether the transient noise can trigger the CNN model or not. In this paper, we
study the responses of the sine-Gaussian glitches, the Gaussian glitches and
the ring-down glitches in the trained convolutional neural network classifier.
We find that the network is robust to the sine-Gaussian and Gaussian glitches,
whose false alarm probabilities are close to that of the LIGO-like noises, in
contrast to the case of the ring-down glitches, in which the false alarm
probability is far larger than that of the LIGO-like noises. We also
investigate the responses of the glitches with different frequency. We find
that when the frequency of the glitches falls in that of the trained GW
signals, the false alarm probability of the glitches will be much larger than
that of the LIGO-like noises, and the probability of the glitches being
misjudged as the GW signals may even exceed 30%.
