Low-resource Asian machine translation draws on monolingual web text from Common Crawl. We ask what share of Bengali Common Crawl a detector flags as machine output, and whether it moves with translation or generation. We introduce BanglaGen-Bench, a 2,700-document Bengali benchmark with human-written, English-to-Bengali machine-translated and LLM-generated classes, and a stylometric detector that needs no Bengali-specific tooling. Translatedness did not transfer between the two NMT systems we tested: a detector trained only on NLLB-200 output recognises mBART-50 output 5.67% of the time, and the reverse 0.00%, while mixed-system training reaches 99.3% on held-out documents of a seen system. A detector trained on dedicated NMT catches an LLM prompted to translate 20 times in 100, against 97 when the same model generates. Across four snapshots (2018–2026), the detector-flagged non-human share rises from 2.13% to 6.20%, and after 2023 the flagged movement is on the translation side: the MT-flagged share climbs from 0.30% to 3.48%, while the LLM-flagged share peaks and falls. These are flagged shares, not validated prevalence: on the only independent Bengali corpus available, which has no translated text, the detector scores 62.7% against 97.3% in-benchmark. The benchmark, checkpoints and scripts will be released.