“Who are the top 100 researchers by both scientific and non scientific volume on the topic of deepfake audio detection and scam audio detection”
A live-web bibliometric scan across 12 query families retrieved 1,849 unique source pages and 8,370 author–work records; after title-level topic filtering, 100 researchers were ranked lexicographically by scientific volume (distinct on-topic scholarly titles per researcher), then verified non-scientific volume, then corroborating scholarly pages. Counts are researcher-level, so a coauthored work counts once for each author. Material runs through the scan date 2026-09-02.
What was measured. Scientific volume counts distinct on-topic scholarly work titles attributed to each researcher in the retrieved corpus; scientific mentions counts distinct scholarly source pages corroborating them. Non-scientific volume counts verified, substantive non-journal pages tied to the person: news, interviews and podcasts, talks, blogs, industry guidance, patents, tool or dataset documentation, policy work, and professional profiles. Topic scope covers audio and speech deepfakes, cloned or synthetic voice, spoofed or replayed speech, and voice-fraud or scam-call detection; it excludes visual-only deepfakes and generic speaker recognition or speech synthesis without a detection focus.
How to read the ranking. Ordering is lexicographic — scientific volume, then non-scientific volume, then scholarly mentions — a two-axis ranking, not a weighted composite. It measures retrieved evidence volume, never citations, impact, quality, or absolute productivity.
Limits. This is a reproducible live-web scan, not a canonical census such as OpenAlex or Scopus. Search and extraction can miss work; initials and common names remain ambiguous (an initials-only entry is not expanded); title variants may inflate counts despite normalization. Non-scientific counts are deliberately conservative: a zero means none verified in this scan, not none exists. Scam-audio-specific literature is sparse and the field often writes under adjacent terms such as speech spoofing, synthetic-speech detection, and voice fraud.
| # | Researcher | Sci. vol. | Public vol. | Sci. pages | Years | Representative publication | Representative public output | Sources |
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Live-web evidence ranking, 100 researcher rows plus one aggregate row. Scan through 2026-09-02 over 12 query families: 1,849 unique source pages, 8,370 author–work records, title-level topic filtering. Scientific volume = distinct on-topic scholarly titles per researcher (researcher-level; coauthored works count for each author); non-scientific volume = verified public output pages only. Table shows one representative publication and output per person for space; more exist in the underlying rows.