---
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.
```