Watermarking Game-Playing Agents in Perfect-Information Extensive-Form Games

• URL: http://arxiv.org/abs/2605.14283v1
• Date: Thu, 14 May 2026 02:33:30 GMT
• Title: Watermarking Game-Playing Agents in Perfect-Information Extensive-Form Games
• Abstract summary: We show how the KGW watermark for LLMs can be adapted to watermark game-playing agents in perfect-information extensive-form games.<n>We show that the degradation in the quality of the watermarked strategy profile, quantified by the expected utility, can be bounded, but there is a tradeoff between detectability and quality.
• Score: 63.23335721758144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Watermarking techniques for large language models (LLMs), which encode hidden information in the output so its source can be verified, have gained significant attention in recent days, thanks to their potential capability to detect accidental or deliberate misuse. Similar challenges involving model misuse also exist in the context of game-playing, such as when detecting the unauthorized use of AI tools in gaming platforms (e.g., cheating in online chess). In this paper, we initiate the study of how game-playing strategies can be watermarked. We show how the KGW watermark for LLMs can be adapted to watermark game-playing agents in perfect-information extensive-form games. The watermark can then be detected using a statistical test. We show that the degradation in the quality of the watermarked strategy profile, quantified by the expected utility, can be bounded, but there is a tradeoff between detectability and quality. In our experiments, we bootstrap the watermarking framework to various chess engines and demonstrate that a) the impact of the watermark on the quality of the strategy is negligible and b) the watermark can be detected with just a handful of games.

Related papers

• Mitigating Watermark Forgery in Generative Models via Randomized Key Selection [33.12939822735328]
  A core security threat are forgery attacks where adversaries insert the provider's watermark into content.<n>Existing defenses resist forgery by embedding many watermarks with multiple keys into the same content.<n>We propose a defense that is provably forgery-resistant emphindependent of the number of watermarked content collected by the attacker.
  arXiv  Detail & Related papers  (2025-07-10T15:52:32Z)
• Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark [6.355836060419373]
  One practical solution is to embed a watermark in the text, allowing ownership verification through watermark extraction.<n>Existing methods primarily focus on defending against modification attacks, often neglecting other spoofing attacks.<n>We propose a technique to detect modifications in text for unbiased watermark which is sensitive to modification.
  arXiv  Detail & Related papers  (2025-02-12T11:56:40Z)
• Steganalysis on Digital Watermarking: Is Your Defense Truly Impervious? [21.06493827123594]
  steganalysis attacks can extract and remove the watermark with minimal perceptual distortion. We show how averaging a collection of watermarked images could reveal the underlying watermark pattern. We propose security guidelines calling for using content-adaptive watermarking strategies and performing security evaluation against steganalysis.
  arXiv  Detail & Related papers  (2024-06-13T12:01:28Z)
• On the Learnability of Watermarks for Language Models [80.97358663708592]
  We ask whether language models can directly learn to generate watermarked text. We propose watermark distillation, which trains a student model to behave like a teacher model. We find that models can learn to generate watermarked text with high detectability.
  arXiv  Detail & Related papers  (2023-12-07T17:41:44Z)
• Unbiased Watermark for Large Language Models [67.43415395591221]
  This study examines how significantly watermarks impact the quality of model-generated outputs. It is possible to integrate watermarks without affecting the output probability distribution. The presence of watermarks does not compromise the performance of the model in downstream tasks.
  arXiv  Detail & Related papers  (2023-09-22T12:46:38Z)
• Towards Robust Model Watermark via Reducing Parametric Vulnerability [57.66709830576457]
  backdoor-based ownership verification becomes popular recently, in which the model owner can watermark the model. We propose a mini-max formulation to find these watermark-removed models and recover their watermark behavior. Our method improves the robustness of the model watermarking against parametric changes and numerous watermark-removal attacks.
  arXiv  Detail & Related papers  (2023-09-09T12:46:08Z)
• An Unforgeable Publicly Verifiable Watermark for Large Language Models [84.2805275589553]
  Current watermark detection algorithms require the secret key used in the watermark generation process, making them susceptible to security breaches and counterfeiting during public detection. We propose an unforgeable publicly verifiable watermark algorithm named UPV that uses two different neural networks for watermark generation and detection, instead of using the same key at both stages.
  arXiv  Detail & Related papers  (2023-07-30T13:43:27Z)
• On the Reliability of Watermarks for Large Language Models [95.87476978352659]
  We study the robustness of watermarked text after it is re-written by humans, paraphrased by a non-watermarked LLM, or mixed into a longer hand-written document. We find that watermarks remain detectable even after human and machine paraphrasing. We also consider a range of new detection schemes that are sensitive to short spans of watermarked text embedded inside a large document.
  arXiv  Detail & Related papers  (2023-06-07T17:58:48Z)
• Evading Watermark based Detection of AI-Generated Content [45.47476727209842]
  A generative AI model can generate extremely realistic-looking content. Watermark has been leveraged to detect AI-generated content. A content is detected as AI-generated if a similar watermark can be decoded from it.
  arXiv  Detail & Related papers  (2023-05-05T19:20:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.