  The increasing realism of synthetic speech, driven by advancements in
text-to-speech models, raises ethical concerns regarding impersonation and
disinformation. Audio watermarking offers a promising solution via embedding
human-imperceptible watermarks into AI-generated audios. However, the
robustness of audio watermarking against common/adversarial perturbations
remains understudied. We present AudioMarkBench, the first systematic benchmark
for evaluating the robustness of audio watermarking against watermark removal
and watermark forgery. AudioMarkBench includes a new dataset created from
Common-Voice across languages, biological sexes, and ages, 3 state-of-the-art
watermarking methods, and 15 types of perturbations. We benchmark the
robustness of these methods against the perturbations in no-box, black-box, and
white-box settings. Our findings highlight the vulnerabilities of current
watermarking techniques and emphasize the need for more robust and fair audio
watermarking solutions. Our dataset and code are publicly available at
\url{https://github.com/moyangkuo/AudioMarkBench}.
