| # MOZAIC: Datasheet |
|
|
| ## 1. Why it was made |
|
|
| MOZAIC is for training and testing audio-visual deepfake detectors in a controlled way. The same |
| real clip appears in all four classes, every clip goes through the same final encoding, and known |
| shortcuts were measured and removed where possible, so a detector has to find the fake, not a side |
| effect of how the data was made. Made by: Vector Institute with AIXPERT. Funding: |
|
|
| ## 2. What is in it |
|
|
| - **Clips:** 137,745 talking-face videos with sound. |
| - **People:** 1,000 from the VoxCeleb2 dev set, half men and half women: public figures in |
| interview and talk videos from YouTube. |
| - **Pairs:** a source clip (person A speaking) and a target person (B, same gender). One pair gives |
| four classes: RVRA, RVFA, FVRA, FVFA. 7,590 pairs at 77 frames, 7,424 at 153 frames. |
| - **Arms:** `main`, and `vocoded` for RVRA and FVRA (the same clip with its voice passed through |
| BigVGAN; the label does not change). |
| - **Lengths:** 77 frames (3.08 s) and 153 frames (6.12 s); the two lengths use different source |
| clips. Animate is 77 frames only: at 6 s its output blurred, which would let a detector find it |
| by blur alone. |
| - **Splits:** by person, with equal numbers of men and women. Both people of a pair are in the same |
| split, and nobody appears in two splits. |
| - **Per clip:** label, generators, length, arm, split, whose face, whose voice, gender. |
| - **Per pair:** the source and target clips used, and which generators kept it. |
| - **Personal data:** VoxCeleb2 ids and gender (from VoxCeleb2's metadata). No names. |
| - **Real clips** are stored once per length and shared by all generators. |
| - **Questions:** four tasks, in `qas/` and `qas_small/`: T1 which part of a clip is fake, T2 how the |
| video was made, T3 whether a clip is real or fake and the same person as a second clip, T4 a written |
| explanation (MOZAIC-Small only). T1 to T3 answers come from the clip's labels. T4 answers were written |
| by language models from those labels and checked by code and a second model. Counts are in `README.md`. |
| - **MOZAIC-Small:** 16,995 clips from 1,858 whole pairs, drawn per split to mirror the full mix (seed 7), |
| with every person the source of at least one pair. It shares the full set's videos. |
|
|
| ## 3. How it was made |
|
|
| - **Source:** VoxCeleb2 dev set, from a public mirror, checked against the official VoxCeleb2 |
| checksums. Each clip was cut from its start to 77 or 153 frames, 224x224, 25 fps, 16 kHz mono. |
| - **Fake faces:** FaceFusion (inswapper_128_fp16) swaps the inner face. Wan2.2-S2V-14B (480p, |
| 40 steps, cfg 4.5) makes the whole video from one picture of the target plus the audio, at 16 fps, |
| retimed to 25 fps. Wan2.2-Animate-14B, replace mode, 480p. |
| - **Fake voices:** Seed-VC (5 steps, cfg 0.3) turns the source's speech into the target's voice, |
| using one reference recording of the target (another of the target's VoxCeleb2 clips, never from |
| the session the face picture came from); it is listed per pair in `pairs.jsonl`. |
| - **Vocoded voices:** real audio passed through BigVGAN v2 (22 kHz), the same vocoder and settings |
| Seed-VC uses, without the conversion. |
| - **Final step, the same for every clip of every class:** brightness, colour and loudness brought |
| to fixed targets; H.264 (crf 18) video and PCM audio; no silence trimmed. For the release, encoder |
| version text was removed from every file by rewriting the container (no re-encoding; every file |
| was checked to decode to exactly the same picture, sound and timing as before). Then every clip |
| got the same 1:1 aspect-ratio tag and the same start of sound (50 ms of silence, then a 10 ms |
| fade-in); every file was checked to decode to exactly its source picture, and to its source sound |
| with that start. |
| - **Reproducibility:** the pair draw used seed 42; every Seed-VC, S2V and Animate job used its own |
| fixed seed, recorded in our build records; FaceFusion has no random sampling. |
|
|
| ## 4. Quality gate |
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|
| Every pair was checked separately for each generator; pairs that failed were removed. |
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|
| | Check | What it tests | Tool | |
| |---|---|---| |
| | Face | The fake looks like the target, not the source | ArcFace (insightface buffalo_l), 8 frames | |
| | Picture | Flicker, melting and sharpness, compared with the real clip | our own measures | |
| | Voice | The fake voice sounds like the target's reference recording; the vocoded voice still sounds like the source | ECAPA speaker model (SpeechBrain) | |
| | Lip sync | Lips match the sound | SyncNet, on a sample of about 10% of pairs | |
| |
| - **Limits:** taken from real clips only (what 95%, or 99% for flicker, of real recordings do), |
| except three judgement calls: face at least 0.30, melt at most 2.0, sharpness at least 0.5. Frozen |
| on 5 Sep 2026, last changed on 14 Sep 2026; the first batch was used to set them. |
| - **Two tests are switched off for one generator each:** "too much of the source face left" for |
| FaceFusion (it keeps the source's hair and head by design) and "melting" for S2V (it invents its |
| own head movement). |
| - **Repeatability:** scoring the same clips twice on different machines gave identical numbers. |
| - **Voice scores** were measured again on the released sound, after the final |
| start-of-sound step, with the gate's own model and code; re-scoring the sound the gate checked |
| reproduced its numbers within 0.0003. The 15 pairs that then fell just below the voice limit were |
| removed, so every released pair passes every gate rule on the released files. |
| - **Human checks:** people did not watch every clip; visual checks were done on samples. |
| |
| Pairs kept per generator: |
| |
| | Generator | Length | Pairs kept | |
| |---|---|---| |
| | facefusion | 77f | 7,020 of 8,000 (88%) | |
| | facefusion | 153f | 6,835 of 8,000 (85%) | |
| | s2v | 77f | 6,360 of 8,000 (80%) | |
| | s2v | 153f | 6,279 of 8,000 (78%) | |
| | animate | 77f | 4,407 of 8,000 (55%) | |
| |
| ## 5. Shortcuts we checked |
| |
| Measured on this release (October 2026) with simple linear classifiers that see only cheap clues: |
| file statistics, picture statistics, frozen ImageNet image features, sound statistics, and the |
| silence at the start of the sound. They were always tested on people they had not seen. AUC 0.50 |
| means no better than guessing and 1.00 a perfect shortcut; 95% ranges in brackets; all clips |
| together unless a generator is named. |
| |
| **Fixed:** |
| |
| | Shortcut | Before | Now | Fix | |
| |---|---|---|---| |
| | How the audio is packed in the file | 0.79 (pilot) | 0.50 | PCM audio with fixed framing | |
| | A header tag (aspect ratio) set on real clips only | 1.00 | gone: every clip has the same file and stream fields | the same 1:1 tag in every clip | |
| | Converted voices fade in over their first 50 ms | 0.74 (0.73-0.75) | 0.57 (0.56-0.57) | 50 ms of silence, then a 10 ms fade-in, at the start of every clip | |
| | Brightness and colour | 0.77 (pilot) | 0.49-0.50 for each | per-clip brightness and colour targets | |
| | Vocoder sound | 0.83 (pilot) | 0.50 on the vocoded view, 0.61 as shipped | the vocoded real arm | |
| |
| **Not fixable:** |
| |
| | Clue | All clips | FaceFusion | Animate | S2V | |
| |---|---|---|---|---| |
| | How the video is packed in the file | 0.86 (0.85-0.86) | 0.73-0.75 | 0.86 | 1.00 | |
| | Picture statistics (sharpness, noise, colour) | 0.71 (0.71-0.72) | 0.68 | 0.73 | 0.79-0.80 | |
| | Frozen ImageNet features (ResNet-18, 8 frames) | 0.87 (0.86-0.87) | 0.82-0.83 | 0.93 | 0.94-0.96 | |
| | Converted voice, sound statistics | 0.90 (0.89-0.90) | same voices for every generator | | | |
| |
| Why they stay: generated video compresses differently from camera video (no encoder setting removed |
| this; only uncompressed video would, about 1.2 TB); the generators draw faces with less fine detail |
| than a camera records; and the voice converter leaves its own trace in the sound. |
| |
| **Advice:** train on decoded frames, not on file bytes or sizes. To test audio without the vocoder |
| trace, use the vocoded view (RVRA vocoded against RVFA, FVRA vocoded and FVFA). Report results per |
| generator and per length. |
| |
| ## 6. Known limitations |
| |
| 1. The way the video is packed in the file still differs between real and generated clips |
| (0.86 AUC from file statistics alone; S2V 1.00). Train on decoded frames. |
| 2. Animate in replace mode redraws the whole frame on VoxCeleb2's tight face crops, so its fakes |
| are closer to a full re-animation (background and clothes can change) than a local face swap. |
| 3. FaceFusion keeps the source's hair and head shape. |
| 4. The voice model used for scoring was trained on VoxCeleb, so it has probably heard these people; |
| its scores may read high. |
| 5. Lip sync was checked on about 10% of pairs only. |
| 6. Three gate limits are judgement calls, and the first batch both set the limits and is released. |
| 7. Each generator kept different pairs, so per-generator sets differ; use `pairs.jsonl`. |
| 8. Same-gender pairs only; gender as recorded by VoxCeleb2 (two values); no age or ethnicity labels. |
| 9. YouTube interview footage of mixed quality; some clips have on-screen text, black borders, or are |
| greyscale. A few clips with very dark, greyscale or peak-limited sources could not reach the |
| brightness, colour or loudness target exactly. |
| 10. Short (3 and 6 seconds), low-resolution (224x224) clips. |
| 11. Generated faces have less fine detail than camera video; generic image features find them |
| (0.87 AUC; FaceFusion 0.82-0.83, Animate 0.93, S2V 0.94-0.96). |
| 12. Converted voices can be told from real ones by simple sound statistics (0.90 AUC). |
| 13. The first 60 ms of every clip's sound was changed (50 ms of silence, then a 10 ms fade-in) to |
| remove a start-of-sound shortcut; a weak trace remains (0.57 AUC). |
| 14. VoxCeleb2 lists one person under two ids (id04048 and id05211, both in training), so there are |
| 999 different people. Two YouTube videos appear under people in different splits (id04177 and |
| id04887; id01036 and id00577); a face check and the gender labels show these are different people. |
| |
| ## 7. Uses |
| |
| - **For:** training and testing deepfake detectors (audio, video or both); studying shortcuts; |
| telling which generator made a fake; studying the effect of clip length. |
| - **Not for:** recognising or identifying people; making deepfakes of the people shown; commercial |
| use; anything that could harm the people in it. |
| |
| ## 8. Ethics and consent |
| |
| - The people in VoxCeleb2 are public figures in public videos; they did not consent to this use. |
| Like other VoxCeleb2-based deepfake datasets (FakeAVCeleb, LAV-DF, AV-Deepfake1M), MOZAIC relies |
| on research-only use and gated access; it adds a removal process. |
| - Every fake shows a real person, so misuse is possible; this is limited by the non-commercial |
| licence, access only after accepting the terms, and short low-resolution clips. |
| - **Removal:** contact TO FILL. We remove that person's clips, and every fake made with them, in the |
| next version. |
| |
| ## 9. Distribution |
| |
| Hugging Face. Private now; gated with manual approval when public. Licence: TO FILL, plus terms of use; credit VoxCeleb2. Version 1.0, released |
| TO FILL. |
| |
| ## 10. Maintenance |
| |
| Maintained by Vector Institute with AIXPERT. Errors are fixed in new versions (1.1 and later) with a change log; removed |
| clips are listed by id. |
| |
