| --- |
| license: cc-by-4.0 |
| task_categories: |
| - audio-classification |
| language: |
| - en |
| tags: |
| - audio |
| - speech |
| - deepfake-detection |
| - anti-spoofing |
| - competition |
| - pelatnas-ioai |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # VoiceGuard — Deepfake Audio Detection Competition |
|
|
| **Pelatnas IOAI 2026 | Task 3 of 3** |
|
|
| Detect whether a 4-second audio clip is real human speech or AI-generated (TTS/deepfake). Submit probability scores — AUROC is the metric. |
|
|
| ## Task |
|
|
| **Input:** `.wav` audio file (4 seconds, 16 kHz mono) |
| **Output:** `score` — probability (0–1) that the audio is **fake** |
| **Metric:** AUROC (Area Under ROC Curve) |
|
|
| ## Dataset |
|
|
| | Split | Real | Fake | Total | |
| |-------|------|------|-------| |
| | Train | 2,874 | 2,874 | 5,748 | |
| | Test | 627 | 627 | 1,254 | |
|
|
| - **Real**: LibriSpeech + VCTK corpus recordings |
| - **Fake**: Generated by ≥3 TTS systems (Tacotron2, VITS, SpeechT5); test includes ≥1 unseen TTS system |
| - Speaker-disjoint: test speakers ≠ train speakers |
|
|
| ## File Structure |
|
|
| ``` |
| ├── train/ |
| │ ├── real/ |
| │ │ └── *.wav |
| │ └── fake/ |
| │ └── *.wav |
| ├── test/ |
| │ └── *.wav # flat, unlabeled |
| ├── train.csv # id, label (real/fake) |
| ├── test.csv # id (no label) |
| ├── sample_submission.csv # id, score=0.5 |
| ├── solution.csv # ground truth: id, label |
| ├── notebooks/ # 6 Colab-ready approaches |
| │ ├── 00_starter.ipynb |
| │ ├── 01_spectral_lr.ipynb # AUROC=1.0000 |
| │ ├── 02_acoustic_rf.ipynb # AUROC=1.0000 |
| │ ├── 03_lfcc_gbdt.ipynb # AUROC=1.0000 |
| │ ├── 04_cnn_mel.ipynb # AUROC=1.0000 |
| │ └── 05_rawnet.ipynb # AUROC=1.0000 |
| ├── submissions/ |
| └── writeup/ |
| └── writeup.md |
| ``` |
|
|
| ## Submission Format |
|
|
| ```csv |
| id,score |
| test/voiceguard_00001.wav,0.92 |
| test/voiceguard_00002.wav,0.04 |
| ... |
| ``` |
|
|
| Score = P(fake). Higher = more likely fake. |
|
|
| ## How to Load |
|
|
| ```python |
| import pandas as pd, librosa |
| train_df = pd.read_csv("train.csv") |
| train_df["label_int"] = (train_df["label"] == "fake").astype(int) |
| y, sr = librosa.load(f"train/{train_df.iloc[0]['id']}", sr=16000) |
| ``` |
|
|
| ## Notebooks (Colab-ready) |
|
|
| All approaches achieve AUROC=1.0 — the TTS artifacts are clearly distinguishable. Focus on understanding **why** each feature works. |
|
|
| | Notebook | Approach | Val AUROC | |
| |----------|----------|-----------| |
| | 01_spectral_lr | Spectral flatness + HNR + MFCC → LogReg | 1.0000 | |
| | 02_acoustic_rf | Full acoustic features → ExtraTrees | 1.0000 | |
| | 03_lfcc_gbdt | LFCC + delta → GradientBoosting | 1.0000 | |
| | 04_cnn_mel | Mel (80×501) + CNN + BCELoss | 1.0000 | |
| | 05_rawnet | Raw waveform + SincConv + GRU | 1.0000 | |
| |
| > **Note**: AUROC=1.0 means all approaches perfectly separate real from fake on this dataset. In production, use harder datasets like ASVspoof 2021 DF or In-the-Wild. |
| |
| ## Why Anti-Spoofing Matters |
| |
| Voice deepfakes enable voice phishing (vishing), identity fraud, and disinformation. Key benchmarks: [ASVspoof 2021](https://www.asvspoof.org/), [ADD Challenge](https://addchallenge.cn/). |
| |
| ## Citation |
| |
| ``` |
| ASVspoof 2019: A large-scale public database of spoofed and genuine speech. |
| Wang et al., Computer Speech & Language, 2020. |
| ``` |
| |
