---
title: 'SpoofCeleb Corpus: Multi-Modal Spoofing'
url: https://www.emergentmind.com/topics/spoofceleb-corpus
type: topic
---

# SpoofCeleb Corpus: Multi-Modal Spoofing

The SpoofCeleb corpus refers to three distinct large-scale, publicly available datasets targeting different modalities and tasks under the umbrella of spoofing detection: (1) speech deepfake detection and spoof-robust speaker verification in the wild [2409.17285], (2) early social media hoax detection with a focus on celebrity death reports [1801.07311], and (3) large-scale face anti-spoofing with comprehensive annotation [2007.12342]. Each resource addresses critical gaps in its respective field by enabling robust, reproducible research on manipulated or falsified content detection under real-world conditions.

## 1. SpoofCeleb for Speech Deepfake and Spoof-Robust ASV

SpoofCeleb [2409.17285] is a large-scale, in-the-wild speech corpus for benchmarking Speech Deepfake Detection (SDD) and Spoofing-robust Automatic Speaker Verification (SASV). It is constructed by applying an automated, TTS-grade preprocessing pipeline to the VoxCeleb1 corpus (1,251 speakers), followed by the synthesis of spoofed utterances using 23 state-of-the-art TTS and voice conversion (VC) systems.

- **Source Data & Preprocessing**: Starting with VoxCeleb1 audio, the TITW-Easy pipeline automates ASR transcription and alignment (WhisperX), silence-based segmentation, heuristic filtering (excluding non-English, too short/long or noisy segments), and neural speech enhancement (DEMUCS). Only bona fide segments (DNSMOS "BAK" score ≥ 3.0) suitable for TTS training are retained, producing ~248,000 high-quality utterances with aligned transcripts. The entire process is scriptable and repeatable for other "in-the-wild" corpora.
  
- **Spoof Generation**: Twenty-three spoofing systems (acoustic+vocoder, end-to-end, neural-codec) are trained on these processed utterances. Attack IDs (A01–A23) capture architectural diversity and configurations (e.g., pretraining, embedding injection).

- **Dataset Structure**: The corpus totals 2,687,292 utterances (1,251 speakers), partitioned into train (2,540,421; A00+A01–A10), validation (55,741; A00+A06,A07,A11–A14), and evaluation splits (91,130; A00+A15–A23), with strict speaker and attack disjointness.

| Partition    | # Files   | Bona fide | Spoof Systems         |
|--------------|-----------|-----------|-----------------------|
| Train        | 2,540,421 | 1,171     | A00 + A01–A10         |
| Validation   |   55,741  | 40        | A00 + A06,A07,A11–A14 |
| Evaluation   |   91,130  | 40        | A00 + A15–A23         |

- **Acoustic Conditions**: The data inherits heterogeneous noise, reverberation, and device diversity from VoxCeleb1 (YouTube interviews), yielding UTMOS of 3.32 and DNSMOS 2.78 (MOS scales).

- **Protocols & Benchmarks**: Train/val/test sets have fully disjoint speakers and spoof types. No cross-validation is performed. Baselines include RawNet2 and AASIST for SDD, and various SASV configurations. Example metrics: SDD Eval EER = 1.12% (RawNet2), Eval EER = 2.37% (AASIST); SASV Eval a-DCF = 0.2902, SV-EER = 12.78%, SPF-EER = 5.00% when trained on SpoofCeleb.

- **Access & Licensing**: Dataset, code, and official protocols are at https://jungjee.github.io/spoofceleb (VoxCeleb1 licensing applies).

This corpus enables systematic evaluation of both SDD and SASV under realistic attack and recording conditions, filling a gap left by prior datasets constrained to clean, studio-quality or low-variability sources [2409.17285].

## 2. SpoofCeleb Corpus for Early Celebrity Death Hoax Detection

The SpoofCeleb corpus [1801.07311]—in this context—designates a large-scale, semantically annotated collection of Twitter death reports ("RIP" events) for the study of early hoax detection and event veracity classification.

- **Construction**: Harvests all English "RIP" tweets (Jan 2012–Dec 2014; 94.2M messages) via the Twitter API. Candidate events are identified when "RIP" is followed by a Wikidata person name/alias and ≥50 such tweets appear on one day. A Wikidata snapshot (Jan 2015) is used for event-to-entity mapping.

- **Annotation Scheme**: Labels—{real, commemoration, fake}—are assigned by semi-automated heuristics exploiting Wikidata death records (±1 day tolerance), supervised by manual validation (Cohen’s κ = 0.982).

- **Corpus Statistics**: Comprises 4,007 labeled events (~13.3M tweets; 2,301 "real", 1,092 "commemoration", 614 "fake"), covering 3,066 distinct individuals. Class imbalance reflects real-world prevalence (15.3% fake).

- **Temporal & Structural Features**: Fake reports cluster over shorter durations (median 50 hours) but can spread virally.

- **Data Organization**: Two files—`reports.csv` (event metadata, labels, intervals) and `tweets.csv` (tweet IDs, timestamps)—enable reproducible experiments. Hydration via Twitter API is required.

- **Early Detection Protocol**: Real-time scenario simulated by releasing tweet batches at time intervals $t \in \{0,1,2,5,10,15,30,60,120,300\}$ minutes post-first tweet. Combined social (engagement stats) and textual features (multiw2v, class-specific Word2Vec) drive a logistic regression classifier. Sliding-window and ablation analyses assess temporal/feature importance.

- **Performance Benchmarks**: At 10 minutes, social+multiw2v achieves $F_1 \approx 0.716$. With only the first tweet ($t=0$) $F_1 \approx 0.669$, quickly rising and plateauing above 0.74 after two hours.

- **Access & Licensing**: Public release via Figshare (DOI: 10.6084/m9.figshare.5688811), CC-BY 4.0, complete with pretrained word-embedding models.

The dataset establishes a realistic, temporally resolved benchmark for research on early detection of rumor, hoax, and memorialization phenomena in social-media streams [1801.07311].

## 3. CelebA-Spoof: Large-Scale Face Anti-Spoofing

Often also referred to as SpoofCeleb [2007.12342], CelebA-Spoof is a comprehensive, identity-labeled face anti-spoofing dataset supporting robust evaluation across environmental and device diversity.

- **Content and Splits**: Contains 625,537 images of 10,177 subjects, split into training (326,514; 5,000 ids), validation (80,513; 1,000 ids), and test (218,510; 4,177 ids) sets. Splits are identity-disjoint, with parity between live and spoofed images per split.

| Split     | Images   | Identities | Live/Spoof Ratio |
|-----------|----------|------------|------------------|
| Train     | 326,514  | 5,000      | ~50/50           |
| Validation| 80,513   | 1,000      | ~50/50           |
| Test      | 218,510  | 4,177      | ~50/50           |

- **Acquisition Diversity**: Recorded under 8 scene conditions (home/office × 4 illuminations) and 20 sensors (smartphones, webcams, tablets, CCTV).

- **Annotation Schema**: Each image is labeled with (1) live/spoof class, (2) one of 10 spoof attack types (varied print/photo/video/3D), and (3) 40 binary facial attributes from the original CelebA dataset.

- **File Organization**: Data are provided as per-subject subfolders, with annotation JSON specifying all metadata. Naming convention encodes subject ID, class, scene, sensor, and frame.

- **Evaluation Protocols**: 
  - Protocol 1—random identity-disjoint split (benchmark)
  - Protocol 2—cross-illumination (generalization to unseen lighting)
  - Protocol 3—cross-environment (office→home transfer)
  - Protocol 4—cross-device (withheld devices at test time)

- **Metrics**: 
  - APCER (Attack Presentation Classification Error Rate): $\mathrm{APCER} = \frac{FP_{\mathrm{spoof}}}{N_{\mathrm{spoof}}}$
  - BPCER (Bona Fide Presentation Classification Error Rate): $\mathrm{BPCER} = \frac{FN_{\mathrm{live}}}{N_{\mathrm{live}}}$
  - ACER (Average Classification Error Rate): $\mathrm{ACER} = \frac{\mathrm{APCER} + \mathrm{BPCER}}{2}$
  - EER (Equal Error Rate): lowest rate at which FPR equals FNR.

- **Auxiliary Information Embedding Network (AENet)**: Multi-task ResNet backbone with three heads (binary spoof-class, spoof type [10-way], attribute vector [40-way]), trained with a composite loss:
  $$
  L_{\mathrm{total}} = L_{\mathrm{cls}} + \lambda_1 L_{\mathrm{type}} + \lambda_2 L_{\mathrm{attr}}
  $$
  Typical $\lambda_1=1.0$, $\lambda_2=0.5$.

- **Benchmark Results**: AENet achieves 0.27% ACER and 0.24% EER (Protocol 1). Protocols focused on unseen environments and devices remain challenging (AENet ACER: 7.80%, baseline 14.30%).

- **Guidelines**: Recommended optimization (Adam, $1\mathrm{e}{-4}$ lr), augmentation (flip, jitter, crop, blur), and considerations for robust evaluation.

CelebA-Spoof enables realistic anti-spoofing algorithm validation with detailed environmental and biometric annotation [2007.12342].

## 4. Comparative Summary of SpoofCeleb Datasets

| Name           | Modality     | Task                        | Size                 | Unique Features                   | Citation      |
|----------------|--------------|-----------------------------|----------------------|------------------------------------|--------------|
| SpoofCeleb (SDD/SASV) | Speech        | Deepfake+ASV robustness       | 2.7M utterances/1,251 spkrs   | 23 TTS/VC systems, in-the-wild, protocol-rich | [2409.17285] |
| SpoofCeleb (Death Hoaxes) | Text/social  | Early rumor/hoax detection    | 13M tweets, 4,007 events      | Real/com/hoax, time-resolved, Wikidata mapping | [1801.07311] |
| CelebA-Spoof   | Face images  | Face anti-spoofing           | 625K images/10,177 ids        | 10 attack types, 40 attributes, 8 scenes, 20 sensors | [2007.12342] |

Each resource enables systematic, protocol-driven research with strong baseline results and facilitates transfer learning and benchmarking across domains.

## 5. Significance and Applications

SpoofCeleb datasets collectively serve as canonical resources for evaluating spoofing detection models under realistic, large-scale, and heterogeneous conditions. They enable:
- Advancement of deepfake and spoof-robust speaker verification, via speaker-disjoint, attack-disjoint, and environment-variable speech benchmarks [2409.17285].
- Rigorous study of temporal veracity classification, social rumor diffusion, and early detection algorithms in naturalistic, imbalanced social streams [1801.07311].
- Comprehensive evaluation of face anti-spoofing systems—including resilience to new sensors, environments, and spoof modalities—through identity-disjoint, well-annotated face imagery [2007.12342].

Researchers can leverage these datasets for benchmarking, transfer learning, protocol comparisons, and development of new discriminative or generative architectures addressing real-world spoofing challenges.

Source: https://www.emergentmind.com/topics/spoofceleb-corpus