  The ability of deep learning (DL) approaches to learn generalised signal and
noise models, coupled with their fast inference on GPUs, holds great promise
for enhancing gravitational-wave (GW) searches in terms of speed, parameter
space coverage, and search sensitivity. However, the opaque nature of DL models
severely harms their reliability. In this work, we meticulously develop a DL
model stage-wise and work towards improving its robustness and reliability.
First, we address the problems in maintaining the purity of training data by
deriving a new metric that better reflects the visual strength of the 'chirp'
signal features in the data. Using a reduced, smooth representation obtained
through a variational auto-encoder (VAE), we build a classifier to search for
compact binary coalescence (CBC) signals. Our tests on real LIGO data show an
impressive performance of the model. However, upon probing the robustness of
the model through adversarial attacks, its simple failure modes were
identified, underlining how such models can still be highly fragile. As a first
step towards bringing robustness, we retrain the model in a novel framework
involving a generative adversarial network (GAN). Over the course of training,
the model learns to eliminate the primary modes of failure identified by the
adversaries. Although absolute robustness is practically impossible to achieve,
we demonstrate some fundamental improvements earned through such training, like
sparseness and reduced degeneracy in the extracted features at different layers
inside the model. We show that these gains are achieved at practically zero
loss in terms of model performance on real LIGO data before and after GAN
training. Through a direct search on 8.8 days of LIGO data, we recover two
significant CBC events from GWTC-2.1, GW190519_153544 and GW190521_074359. We
also report the search sensitivity obtained from an injection study.
