Like many other tasks involving neural networks, Speech Recognition models
are vulnerable to adversarial attacks. However recent research has pointed out
differences between attacks and defenses on ASR models compared to image
models. Improving the robustness of ASR models requires a paradigm shift from
evaluating attacks on one or a few models to a systemic approach in evaluation.
We lay the ground for such research by evaluating on various architectures a
representative set of adversarial attacks: targeted and untargeted,
optimization and speech processing-based, white-box, black-box and targeted
attacks. Our results show that the relative strengths of different attack
algorithms vary considerably when changing the model architecture, and that the
results of some attacks are not to be blindly trusted. They also indicate that
training choices such as self-supervised pretraining can significantly impact
robustness by enabling transferable perturbations. We release our source code
as a package that should help future research in evaluating their attacks and
defenses.
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