  Previous works have validated that text generation APIs can be stolen through
imitation attacks, causing IP violations. In order to protect the IP of text
generation APIs, a recent work has introduced a watermarking algorithm and
utilized the null-hypothesis test as a post-hoc ownership verification on the
imitation models. However, we find that it is possible to detect those
watermarks via sufficient statistics of the frequencies of candidate
watermarking words. To address this drawback, in this paper, we propose a novel
Conditional wATERmarking framework (CATER) for protecting the IP of text
generation APIs. An optimization method is proposed to decide the watermarking
rules that can minimize the distortion of overall word distributions while
maximizing the change of conditional word selections. Theoretically, we prove
that it is infeasible for even the savviest attacker (they know how CATER
works) to reveal the used watermarks from a large pool of potential word pairs
based on statistical inspection. Empirically, we observe that high-order
conditions lead to an exponential growth of suspicious (unused) watermarks,
making our crafted watermarks more stealthy. In addition, \cater can
effectively identify the IP infringement under architectural mismatch and
cross-domain imitation attacks, with negligible impairments on the generation
quality of victim APIs. We envision our work as a milestone for stealthily
protecting the IP of text generation APIs.
