A case study on Chapter 5 of Chinese Literature: A Very Short Introduction (Knight) found 7 censored topics, 5 removed paragraphs, 31 removed sentences, and 2 removed/altered words in the Chinese translation. Censored topics included 2000 Nobel laureate Gao Xingjian, the Tiananmen Square Massacre, Mao Zedong, the Cultural Revolution, the Great Leap Forward, the plasma economy scandal in Henan, and a discussion of book censorship itself.

In China, it is typically the publisher (not the government) who self-censors translated books to avoid punishment including harsh scrutiny of future publications, book confiscation, and suspension of publishing rights. Authors are often unaware their translations were altered until well after publication. This self-censorship dynamic produces more restrictive outcomes than direct government censorship because publishers err on the side of caution without clear rules.

§1 Introduction policy

ChatGPT correctly identified missing sentences in a partially censored translation and correctly judged a complete translation as complete in a control condition, demonstrating that LLMs are a viable complementary detection method. The paper notes that having multiple independent detection approaches (NLP alignment, bitext mining, LLM-based reasoning) improves overall robustness by enabling cross-validation.

§4 Discussion / Appendix C detection

The paper proposes detecting translation censorship by back-translating the Chinese text to English via Google Translate, embedding each paragraph with distiluse-base-multilingual-cased-v1, and solving a linear-sum-assignment bipartite matching weighted by negated cosine similarity. Paragraphs below a similarity threshold are flagged as cut; matched paragraphs are recursively compared at sentence level to detect alterations.

§2 Methodology detection

The paper argues that an effective counter to translation censorship is to actively trigger the Streisand effect: publishing detected censored content side-by-side with the original on a public website causes the censored text to reach a broader audience — including people who would not have read the censored version — and makes the censorship itself backfire. Censors deliberately avoid publicizing removals precisely to prevent this outcome.

§1 Introduction / §5 Conclusion defense

During the June 2025 shutdown, Iranian authorities blocked international One-Time Password (OTP) SMS delivery, preventing new sign-ins to foreign secure-messaging platforms and VPN services. This forced users toward government-approved domestic platforms that lack security and privacy protections. The blockade of OTPs effectively weaponized account-recovery flows as a secondary shutdown layer, disproportionately affecting users who needed to activate new circumvention tools during the crisis.

2025-iran-shutdown-measurement §Human Rights Implications port-blockingdpikeyword-filtering

Simple character-level perturbations (English) and homophone substitutions (Chinese), combined with LLM instruction-following prompts directing the model to use word substitutions in its output, successfully bypassed all input and output filters for all 41 input-blocked and 197 output-blocked queries across five major Chinese LLM services (Baidu-Chat, DeepSeek, Doubao, Kimi, Qwen). Every input-blocked query contained at least one keyword combination that alone triggered the filter, confirming keyword-matching rather than semantic classification.

2026-ablove-characterizing §VII-D keyword-filtering

Cross-national experiments conducted from Singapore, South Korea, and Taiwan during February 19–24, 2025 found no variance in blocking implementations, event syntax, or server infrastructure across all five Chinese LLM services. Input blocking was enacted identically in all three international locations, and services connected to the exact same server IP addresses globally — Kimi and Baidu-Chat connected to identical IPs and DeepSeek to the same two addresses across all tested locations.

2026-ablove-characterizing §VIII-A keyword-filtering

Input blocking in Chinese LLM services (DeepSeek, Qwen, Kimi, Doubao) is overwhelmingly consistent: all four services persistently block the exact same queries across all 5 measurement samples in both Simplified and Traditional Chinese. Output blocking is far less consistent, with only 29 out of 349 output-blocked queries blocked across all 5 samples. Baidu-Chat is exceptional: it performs almost no input blocking but instead relies heavily on post-search and output blocking (78.6% of blocks are output-phase).

2026-ablove-characterizing §VI-B keyword-filteringdpi

DeepSeek, Kimi, and Doubao all transmit analytics logs to the same Autonomous System (AS24429, Zhejiang Taobao Network Co., Ltd., a ByteDance/Volcengine subsidiary), with one monitoring endpoint IP directly overlapping between DeepSeek and Doubao. Additionally, all four non-Qwen services maintain connections with servers physically located in China throughout the chat session, transmitting user IDs, session IDs, viewport data, language preferences, and in Baidu-Chat's case, the full query text via URL-encoded CAPTCHA requests.

2026-ablove-characterizing §VI-D keyword-filtering

All five Chinese LLM services transmit partial or complete responses to the client machine even when output blocking is triggered, representing a major information leak. For DeepSeek and Qwen, truncated blocked responses are on average close in token length to full successful responses. For Baidu-Chat, the complete response is transmitted to the client but only partially rendered in the browser UI, with only a word or two visible on screen.

2026-ablove-characterizing §VI-C keyword-filtering