| --- |
| pretty_name: MOZAIC |
| language: |
| - en |
| tags: |
| - deepfake |
| - deepfake-detection |
| - audio-visual |
| - video |
| - audio |
| task_categories: |
| - video-classification |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: t1_spot_the_fake |
| default: true |
| data_files: |
| - split: train |
| path: qas/t1_spot_the_fake/t1_train.jsonl |
| - split: val |
| path: qas/t1_spot_the_fake/t1_val.jsonl |
| - split: test |
| path: qas/t1_spot_the_fake/t1_test.jsonl |
| - config_name: t2_manipulation_type |
| data_files: |
| - split: train |
| path: qas/t2_manipulation_type/t2_train.jsonl |
| - split: val |
| path: qas/t2_manipulation_type/t2_val.jsonl |
| - split: test |
| path: qas/t2_manipulation_type/t2_test.jsonl |
| - config_name: t3_identity_check |
| data_files: |
| - split: train |
| path: qas/t3_identity_check/t3_train.jsonl |
| - split: val |
| path: qas/t3_identity_check/t3_val.jsonl |
| - split: test |
| path: qas/t3_identity_check/t3_test.jsonl |
| - config_name: t1_spot_the_fake_small |
| data_files: |
| - split: train |
| path: qas_small/t1_spot_the_fake/t1_train.jsonl |
| - split: val |
| path: qas_small/t1_spot_the_fake/t1_val.jsonl |
| - split: test |
| path: qas_small/t1_spot_the_fake/t1_test.jsonl |
| - config_name: t2_manipulation_type_small |
| data_files: |
| - split: train |
| path: qas_small/t2_manipulation_type/t2_train.jsonl |
| - split: val |
| path: qas_small/t2_manipulation_type/t2_val.jsonl |
| - split: test |
| path: qas_small/t2_manipulation_type/t2_test.jsonl |
| - config_name: t3_identity_check_small |
| data_files: |
| - split: train |
| path: qas_small/t3_identity_check/t3_train.jsonl |
| - split: val |
| path: qas_small/t3_identity_check/t3_val.jsonl |
| - split: test |
| path: qas_small/t3_identity_check/t3_test.jsonl |
| - config_name: t4_explanation_small |
| data_files: |
| - split: train |
| path: qas_small/t4_explanation/t4_train.jsonl |
| - split: val |
| path: qas_small/t4_explanation/t4_val.jsonl |
| - split: test |
| path: qas_small/t4_explanation/t4_test.jsonl |
| --- |
| |
| <p align="center"> |
| <img src="assets/logo-icon-black.png" alt="Vector Institute" width="150" style="display: inline-block; vertical-align: middle;"> |
| |
| <img src="assets/AIXPERTLogo.png" alt="AIXPERT" width="220" style="display: inline-block; vertical-align: middle;"> |
| </p> |
|
|
| # 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. |
|
|
|  |
|
|
| ## 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/<label>/<generator>/<length>/<arm>/*.mov`, where |
| `<generator>` is `real` for RVRA and `seed_vc` for RVFA. A tar holds each clip unchanged, so its bytes |
| are the ones `SHA256SUMS` lists. |
|
|
| To get only the test split's clips (5.5 GB): |
|
|
| ```bash |
| hf download vector-institute/Mozaic --repo-type dataset --local-dir mozaic --exclude "videos/train.tar" --exclude "videos/val.tar" |
| cd mozaic |
| tar -xf videos/test.tar |
| sha256sum -c --ignore-missing SHA256SUMS |
| ``` |
|
|
| To get one clip, take its `split` and `file_name` from `metadata.jsonl`. It is in `videos/<split>.tar`: |
|
|
| ```bash |
| tar -xf videos/test.tar videos/FVFA/animate/77f/main/FVFA__animate__77f__main__id00024_3ysHAigPe68_00005__id00330.mov |
| ``` |
|
|
| ## File names |
|
|
| `<label>__<generator>__<length>__<arm>__<source_clip>__<target_id>.mov` |
|
|
| Example: `FVFA__s2v__153f__main__id00058_6PiUqI-q-1w_00025__id03180.mov` |
|
|
| A clip id such as `id00058_6PiUqI-q-1w_00025` is the VoxCeleb2 clip `id00058/6PiUqI-q-1w/00025`: the |
| person is the first 7 characters, the segment the last 5, and the YouTube video id the part in between. |
| Video ids can contain `_` and `-`, so do not split names on underscores; read the fields from |
| `metadata.jsonl`. |
|
|
| ## Video and audio |
|
|
| `.mov`, H.264, 224x224, 25 fps; audio is uncompressed PCM, 16 kHz, mono. Every clip was brought to the |
| same brightness, colour and loudness targets (see `summary.json`), and encoder version text was removed |
| from every file. Every clip carries the same 1:1 aspect-ratio tag, and every clip's sound starts with |
| 50 ms of silence and a 10 ms fade-in (converted voices used to fade in on their own). This largely |
| removes these differences between real and fake; `DATASHEET.md` (section 5) lists what was measured and |
| what remains. |
|
|
| ## Question files |
|
|
| One row per question, one file per task and split. Clip paths are relative to the dataset folder. |
|
|
| | Field | Meaning | |
| |---|---| |
| | `id` | the question's id | |
| | `task` | the task's name | |
| | `split` | train, val or test | |
| | `clips` | the clip paths: one, or two for T3 | |
| | `question` | the question text | |
| | `options` | the answers to choose from, shuffled per question; for T4 the regions | |
| | `part` | T3 only: face or voice | |
| | `gt_answer` | the right answer; for T4 the written answer | |
| | `gt_label` | the clip's label: RVRA, RVFA, FVRA or FVFA | |
| | `gt_target` | the answer as data; for T4 the fake parts, their regions and each part's facts | |
| | `gt_target_name` | T2 and T3 only: the answer as an id | |
| | `meta` | the clip's generators, frames, arm, people and pair; T3 also says whose recording clip 2 is | |
|
|
| ## Manifests |
|
|
| - `metadata.jsonl`, one row per video (137,745 rows), everything about it in one place. |
|
|
| | Field | Type | Meaning | |
| |---|---|---| |
| | `file_name` | string | path of the video inside the dataset folder | |
| | `label` | string | RVRA, RVFA, FVRA or FVFA | |
| | `video_generator` | string | real, facefusion, s2v or animate | |
| | `audio_generator` | string | none, seed_vc or bigvgan | |
| | `length` | string | 77f (3.08 s) or 153f (6.12 s) | |
| | `arm` | string | main or vocoded | |
| | `split` | string | train, val or test | |
| | `face_id` | string | whose face you see (VoxCeleb2 person id) | |
| | `voice_id` | string | whose voice you hear (VoxCeleb2 person id) | |
| | `gender` | string | m or f | |
| | `face_real` | bool | the face is real | |
| | `voice_real` | bool | the voice is real | |
| | `pair_id` | string | the pair the video belongs to | |
| | `source_id` | string | the source person: who is in the original clip | |
| | `target_id` | string | the target person: who the fake turns them into | |
| | `source_clip` | string | the VoxCeleb2 clip the video was made from | |
| | `target_face_clip` | string | the target's clip the face picture came from | |
| | `target_voice_clip` | string | the target's recording Seed-VC used as its voice reference | |
| | `kept_by` | list of strings | the generators that kept the pair | |
| | `sha256` | string | checksum of the video file | |
|
|
| - `metadata_table.parquet`: the same rows and fields as `metadata.jsonl`, with the types above stored in |
| the file, so it loads much faster. |
| - `pairs.jsonl`, one row per pair: `pair_id`, `length`, `split`, `gender`, `source_id`, `target_id`, |
| `source_clip`, `target_clips` (`face`: the target clip the face picture came from; `voice`: the target |
| recording Seed-VC used as its voice reference) and `kept_by` (the generators that kept the pair). |
| - `splits.json`: person -> split. |
| - `summary.json`: version, settings, generators and every count. |
| - `SHA256SUMS`: the sha256 of every clip, manifest and question file. It leaves out `README.md`, |
| `DATASHEET.md`, `summary.json` and `summary_small.json`, which are text and may still be edited. |
|
|
| ## Loading |
|
|
| `requirements.txt` lists the packages these examples use. The fastest way to read the clip table is |
| `metadata_table.parquet`: |
|
|
| ```python |
| import pandas as pd |
| |
| clips = pd.read_parquet("mozaic/metadata_table.parquet") |
| test_fakes = clips[(clips["split"] == "test") & ~clips["face_real"]] |
| ``` |
|
|
| A task's questions: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| t1_test = load_dataset("vector-institute/Mozaic", "t1_spot_the_fake", split="test") |
| row = t1_test[0] |
| print(row["question"], row["options"], row["gt_answer"], row["clips"]) |
| ``` |
|
|
| **One generator only.** Real clips are stored once and shared by all generators, but each generator |
| kept different pairs. To build a clean set for one generator, keep the pairs it kept (`kept_by`): |
|
|
| ```python |
| facefusion_77f = clips[(clips["length"] == "77f") |
| & clips["kept_by"].apply(lambda kept: "facefusion" in kept) |
| & clips["video_generator"].isin(["real", "facefusion"])] |
| ``` |
|
|
| ## Good to know |
|
|
| - Splits are by person: no face or voice in the test split appears in training. |
| - Train on decoded frames, not on file bytes or sizes: how the video is packed still differs between |
| real and generated clips (`DATASHEET.md`, section 5). |
| - To test audio without the vocoder trace, use the vocoded view: RVRA vocoded against RVFA, FVRA vocoded |
| and FVFA. |
| - Known limitations are listed in `DATASHEET.md`. |
|
|
| ## Access and licence |
|
|
| This repository is private for now. When it is public, access will be gated with manual approval. |
|
|
| Licence: TO FILL. MOZAIC is built on VoxCeleb2, so please also cite VoxCeleb2. |
|
|
| If you appear in a clip and want it removed, contact: TO FILL. We remove that person's clips, and |
| every fake made with them, in the next version. |
|
|
| ## Citation |
|
|
| MOZAIC: TO FILL |
|
|
| VoxCeleb2: Chung, Nagrani, Zisserman. *VoxCeleb2: Deep Speaker Recognition.* Interspeech 2018. |
|
|
| ## Changes |
|
|
| - 1.0: first version. |
|
|
