ADG is a forum to exchange ideas and views, to present research results and
progress, and to demonstrate software tools at the intersection between
geometry and automated deduction. The conference is held every two years. The
previous editions of ADG were held in Hagenberg in 2021 (online, postponed from
2020 due to COVID-19), Nanning in 2018, Strasbourg in 2016, Coimbra in 2014,
Edinburgh in 2012, Munich in 2010, Shanghai in 2008, Pontevedra in 2006,
Gainesville in 2004, Hagenberg in 2002, Zurich in 2000, Beijing in 1998, and
Toulouse in 1996.
The 14th edition, ADG 2023, was held in Belgrade, Serbia, in September 20-22,
2023. This edition of ADG had an additional special focus topic, Deduction in
Education.
Invited Speakers: Julien Narboux, University of Strasbourg, France
"Formalisation, arithmetization and automatisation of geometry"; Filip Mari\'c,
University of Belgrade, Serbia, "Automatization, formalization and
visualization of hyperbolic geometry"; Zlatan Magajna, University of Ljubljana,
Slovenia, "Workshop OK Geometry"

ADG 是一个关于几何和自动推理交叉领域的论坛，每两年举办一次，为交流思想、展示研究成果和进展，以及展示软件工具提供了平台，2023 年的 ADG 会议在塞尔维亚贝尔格莱德举办，并且特别关注于教育中的推理问题。

第 14 届国际自动几何推理会议论文集

Proceedings 14th International Conference on Automated Deduction in  Geometry

Generative linguistic steganography mainly utilized language models and
applied steganographic sampling (stegosampling) to generate high-security
steganographic text (stegotext). However, previous methods generally lead to
statistical differences between the conditional probability distributions of
stegotext and natural text, which brings about security risks. In this paper,
to further ensure security, we present a novel provably secure generative
linguistic steganographic method ADG, which recursively embeds secret
information by Adaptive Dynamic Grouping of tokens according to their
probability given by an off-the-shelf language model. We not only prove the
security of ADG mathematically, but also conduct extensive experiments on three
public corpora to further verify its imperceptibility. The experimental results
reveal that the proposed method is able to generate stegotext with nearly
perfect security.

本文提出一种名为 ADG 的具有可证明安全性的生成语言隐写方法，该方法使用自适应动态分组的令牌递归嵌入秘密信息以提高安全性。 通过在三个公共语料库上进行广泛实验，证明了 ADG 的不可察觉性并成功生成几乎完美安全的隐写文本。