--- pretty_name: MOZAIC language: - en tags: - deepfake - deepfake-detection - audio-visual - video - audio task_categories: - video-classification size_categories: - 100K Vector Institute      AIXPERT

# MOZAIC MOZAIC is an audio-visual deepfake dataset built from VoxCeleb2, with question files for testing models. Every clip is a talking face with sound, in one of four classes: real or fake face, crossed with real or fake voice. The fakes come from three video generators and one voice converter. - Pairs at two lengths: 7,590 at 77 frames (3.08 s) and 7,424 at 153 frames (6.12 s) - Split by person: nobody in the test split appears in training - Four tasks with question files: T1 to T3 on the full set and on MOZAIC-Small, T4 on MOZAIC-Small - MOZAIC-Small: a smaller set of whole pairs that mirrors the full one `DATASHEET.md` says how it was made, what was checked and what its limits are. `summary.json` has every count. Made by Vector Institute with AIXPERT. ## Statistics | | Clips | Hours | People | Pairs | Questions | Tasks | |---|---|---|---|---|---|---| | MOZAIC | 137,745 | 169.9 | 1,000 ids | 15,014 | 289,587 | T1 to T3 | | MOZAIC-Small | 16,995 | 21.0 | the same 1,000 ids | 1,858 | 47,087 | T1 to T4 | | Split | Men | Women | Pairs | Clips | |---|---|---|---|---| | train | 350 | 350 | 10,541 | 96,957 | | val | 75 | 75 | 2,232 | 20,553 | | test | 75 | 75 | 2,241 | 20,235 | Gender is as recorded by VoxCeleb2. Across all clips, 51.4% show men and 48.6% show women. MOZAIC has no age or race labels. The 1,000 ids are 999 people, because VoxCeleb2 lists one person under two ids. ![Bar charts of questions per task, clips by gender, clips per class and clips per face source](assets/statistics.png) ## The four classes | Label | Face | Voice | Clips | |---|---|---|---| | RVRA | real | real | 30,028 | | RVFA | real | fake | 15,014 | | FVRA | fake | real | 61,802 | | FVFA | fake | fake | 30,901 | ## Generators | Name | Makes | What it does | Clips | |---|---|---|---| | facefusion | fake face | Swaps the inner face; hair and head stay the original person's | 41,565 | | s2v | fake face | Wan2.2-S2V: makes the whole video from one picture of the target plus the audio | 37,917 | | animate | fake face | Wan2.2-Animate (replace mode); 77 frames only | 13,221 | | seed_vc | fake voice | Seed-VC voice conversion | 45,915 | **Source and target:** the *source* is the person in the original clip; the *target* is the person the fake turns them into. (FaceFusion's own documentation uses these two words the other way round.) ## Arms - `main`: the clip as made. - `vocoded`: the same clip with its real voice passed through BigVGAN, the vocoder Seed-VC uses. The label does not change: a vocoded RVRA clip is still real. This stops "vocoder sound = fake" from being a shortcut. ## Tasks | Task | Question | Answers | Input | Full: train / val / test | Small: train / val / test | |---|---|---|---|---|---| | T1 spot the fake | Which part of this audio-visual clip, if any, is AI-generated or manipulated? | the video, the audio, both, neither | frames and sound | 64,638 / 13,702 / 13,490 | 6,664 / 1,332 / 3,334 | | T2 manipulation type | Which part of this video, if any, was generated or altered by AI? | real, face swap, full-body swap, fully generated | frames | 32,319 / 6,851 / 6,745 | 3,332 / 666 / 1,667 | | T3 identity check | Is the face (or voice) in clip 1 real or fake, and is it the same person as in clip 2? | real, same person; real, different person; fake of this person; fake of a different person | frames of two clips, or sound of two clips | 106,802 / 22,586 / 22,454 | 11,012 / 2,200 / 5,550 | | T4 explanation | Which parts of this audio-visual clip, if any, were generated or altered by AI, where, and what in the clip shows it? | a written answer, with its facts in `gt_target` | frames and sound | none | 6,664 / 1,332 / 3,334 | Which clips each task asks about: - **T1 and T4:** the vocoded view: RVRA vocoded, RVFA main, FVRA vocoded and FVFA main. - **T2:** RVRA main and FVRA main. It is only about the video. - **T3:** clip 1 is RVRA main or FVRA main for a face question, RVRA vocoded or RVFA main for a voice question. Clip 2 is a real recording (RVRA main) of the pair's source or target, of the same length, from a YouTube video the pair never used. Each clip 1 is asked twice, once for each person. Four val pairs (source id09056, target id05938) are left out of T3 because the target has no such recording; T1 and T2 still use them. T1 to T3 answers come from the clip's labels. T4 answers were written by language models from the clip's facts, then checked by code and by a second model; each row's `meta` names the models, and those models should not be tested on T4. `meta.human_checked` is true on 400 test rows, the set three people reviewed. Do not show a model the file names or question ids: they contain the label. ## MOZAIC-Small Whole pairs, each Small split drawn from the same full split, in the same mix as the full set, with every person the source of at least one pair. Its files sit next to the full ones with `_small` added, and every row is the full set's row, unchanged. Small has no clips of its own: it uses the same `videos/`. T3 on Small also uses 2,913 real clips outside `metadata_small.jsonl` as clip 2. They are listed in `metadata.jsonl`. ## Folders ``` mozaic/ ├── README.md, DATASHEET.md, requirements.txt, assets/ ├── metadata.jsonl one row per clip with everything about it ├── metadata_table.parquet the same rows, typed, for fast loading ├── pairs.jsonl one row per pair: its people, clips and the generators that kept it ├── splits.json person -> train, val or test ├── summary.json version, settings, generators, all counts ├── the same four files for Small, with _small in their names (no splits file: Small uses the same one) ├── SHA256SUMS the sha256 of every clip, manifest and question file ├── qas/ T1, T2 and T3: one folder per task, with train, val and test files ├── qas_small/ the same for Small, and T4 └── videos/ ├── train.tar 26.2 GB ├── val.tar 5.6 GB └── test.tar 5.5 GB ``` ## Download and unpack The whole dataset is 38 GB. Unpacking needs the same space again, until you delete the tars. ```bash pip install huggingface_hub hf download vector-institute/Mozaic --repo-type dataset --local-dir mozaic cd mozaic for t in videos/*.tar; do tar -xf "$t"; done sha256sum -c SHA256SUMS ``` Every clip then sits at the path in `file_name`: `videos/