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leeytkfng/README.md

이용수 (Yong-Su Lee)

AI Researcher in Audio Deepfake Detection
μ˜€λ””μ˜€ λ”₯페이크 탐지 Β· μŒμ› 뢄리 Β· μ„€λͺ… κ°€λŠ₯ν•œ AI


πŸ”¬ Research

ν˜„μž¬ 볡합 μ˜€λ””μ˜€ μœ„λ³€μ‘° 탐지 연ꡬλ₯Ό μ§„ν–‰ μ€‘μž…λ‹ˆλ‹€.
κΈ°μ‘΄ λͺ¨λΈμ΄ "μ§„μ§œ/κ°€μ§œ" 이진 νŒλ³„μ— κ·ΈμΉ˜λŠ” 것과 달리, μŒμ„±κ³Ό ν™˜κ²½μŒμ„ λΆ„λ¦¬ν•˜μ—¬ "μ–΄λŠ 채널이 κ°€μ§œμΈκ°€" λ₯Ό 5개 클래슀둜 μ„€λͺ…ν•˜λŠ” ν”„λ ˆμž„μ›Œν¬λ₯Ό μ œμ•ˆν•©λ‹ˆλ‹€.

관심 연ꡬ λΆ„μ•Ό

  • μ˜€λ””μ˜€ λ”₯페이크 탐지 (Audio Anti-Spoofing)
  • μŒμ› 뢄리 (Blind Source Separation)
  • μ„€λͺ… κ°€λŠ₯ν•œ AI (Explainable AI, XAI)
  • μŒμ„±/μ˜€λ””μ˜€ μ‹ ν˜Έ 처리

πŸš€ Featured Research Project

μŒμ› 뢄리 + 물리 음ν–₯ ν”Όμ²˜ 기반 5-class 볡합 μ˜€λ””μ˜€ μœ„λ³€μ‘° 탐지 μ‹œμŠ€ν…œ

ν•­λͺ© λ‚΄μš©
데이터셋 CompSpoofV2 (24,864 samples, 5-class)
핡심 λͺ¨λΈ Conv-TasNet (μŒμ› 뢄리) + LightGBM (19-dim 물리 ν”Όμ²˜)
Accuracy 69.8% Β· Macro-F1 0.6564
FAKE Recall 56.4% (SuDORM-RF λŒ€λΉ„ +41%p κ°œμ„ )
λ…Όλ¬Έ μ •λ³΄μ²˜λ¦¬ν•™νšŒ λ…Όλ¬Έ μž‘μ„± 쀑 (2026)

핡심 κΈ°μ—¬

  • μ‚¬μ „ν•™μŠ΅ 없이 물리 음ν–₯ μ§€ν‘œ(RT60, Noise Floor, MSC, XCorr)만으둜 경쟁λ ₯ μžˆλŠ” 탐지 μ„±λŠ₯ 달성
  • SI-SNR ↔ FAKE recall λΉ„μ„ ν˜• 상관관계 μ •λŸ‰ 싀증
  • LightGBM Feature Importanceλ₯Ό ν†΅ν•œ νŒμ • κ·Όκ±° μ‹œκ°ν™”
[μž…λ ₯ μ˜€λ””μ˜€]
     β”‚
[LCNN-SE Gatekeeper]  β†’  REAL μ‘°κΈ° νŒμ •
     β”‚
[Conv-TasNet μŒμ› 뢄리]
     β”œβ”€β”€ πŸ—£οΈ Speech Stream β†’ WavLM μœ„μ‘° 점수
     └── 🌿 Env Stream   β†’ LCNN-SE μœ„μ‘° 점수
     β”‚
[물리 음ν–₯ 뢄석: RT60 Β· Noise Floor Β· MSC Β· XCorr]
     β”‚
[LightGBM 5-class] β†’ REAL / GENUINE / SPOOF_SPEECH / SPOOF_ENV / FAKE

πŸ›  Tech Stack

AI / ML
Python PyTorch LightGBM scikit-learn HuggingFace

Audio / Signal Processing
librosa torchaudio SpeechBrain

Backend (κΈ°μ‘΄ κ²½ν—˜)
Java Spring Boot PostgreSQL Docker


πŸ“„ Publication

μƒνƒœ 제λͺ© ν•™μˆ λŒ€νšŒ
μž‘μ„± 쀑 S-CAD: μŒμ› 뢄리 기반 볡합 μ˜€λ””μ˜€ μœ„λ³€μ‘° 5-class 탐지 μ •λ³΄μ²˜λ¦¬ν•™νšŒ 2026

πŸ“« Contact


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