Defeating LLM Watermarks & Detection Heuristics

Mechanisms to break statistical watermarks and perplexity/burstiness detectors: - Paraphrase using a local model (Llama-3, Mistral, T5) – re-generates semantics from scratch, destroys the original token bias. - Back‑translation – en→fr→en via argos-translate or Google Translate. Breaks n‑gram correlations without altering meaning. - Synonym substitution + voice switching – replace common words with obscure synonyms (WordNet) and toggle active/passive. Example: replace every "however" with "yet", change "the system was exploited" → "they exploited the system". - Increase perplexity – introduce uncommon vocabulary, complex clauses, variable sentence length. Detection models flag "too perfect" text. - Break burstiness – deliberately vary sentence length and structure; add occasional minor grammatical errors to mimic human writing. Toolchain: wordnet, spacy for POS‑aware substitution, argos-translate for back‑translation, or a local model for full paraphrase. submitted by /u/tcoder7 [link] [comments]

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