DeepFense Paper Scores
Canonical, reproducible score bundle for the DeepFense camera-ready paper.
Hugging Face: DeepFense/prediction_scores
Directory layout
scores/
├── README.md
├── {train_recipe}/ # dataset the model was TRAINED on
│ ├── {backend}/ # AASIST | MLP | Nes2Net | TCM
│ │ └── {frontend}/ # Wav2Vec2 | HuBERT | WavLM | EAT
│ │ └── seed{2|42|240}/
│ │ └── {eval_benchmark}/ # held-out TEST set
│ │ ├── predictions.txt # per-utterance scores (TSV)
│ │ └── metrics.json # EER, ACC, F1, …
│ └── _summaries/{eval_benchmark}.json # optional cross-architecture tables
└── bias_fairness/
└── {accent|emotions|gender|language|quality}/
└── {eval_benchmark}/
└── {train_recipe}/{backend}/{frontend}/seed{N}/
└── utterances.txt # scores + subgroup metadata (TSV)
Example
From checkpoint DeepFense_ADD23_Wav2Vec2_TCM_NoAug_Seed240 evaluated on add22_test_track1:
ADD23/TCM/Wav2Vec2/seed240/add22_test_track1/predictions.txt
ADD23/TCM/Wav2Vec2/seed240/add22_test_track1/metrics.json
Naming conventions
| Token | Canonical form | Notes |
|---|---|---|
| Train recipe | ASV5, ASV19, ADD23, CodecFake, HABLA, PartialSpoof |
Training dataset (not the eval set). ASV5 = trained on ASVspoof 5; do not confuse with eval asvspoof5_test. |
| Frontend | Wav2Vec2, HuBERT, WavLM, EAT |
Always PascalCase; Hubert → HuBERT. |
| Backend | AASIST, MLP, Nes2Net, TCM |
Uppercase acronym. |
| Seed | seed2, seed42, seed240 |
Three seeds per recipe. |
| Eval benchmark | lowercase snake_case | e.g. asvspoof5_test, asvspoof2019_la_eval, mlaad_final, add22_test_track1. |
Eval benchmarks (20)
add22_test_track1, add22_test_track3, add23_test_R1, add23_test_R2, asvspoof2019_la_eval, asvspoof21_df_eval, asvspoof21_la_eval, asvspoof5_test, codecfake_eval, ctrsvdd_eval, fakemusiccaps_eval, habla_test, itw_eval, mlaad_final, odss_test, partialedit_eval, partialspoof_eval, replaydf_all_eval, spoofceleb_eval
File formats (.txt / .json only)
predictions.txt — clip-level (TSV)
utterance_id label score_spoof score_bonafide
LA_E_12345 0 -2.14895 3.14895
LA_T_67890 1 4.37140 -3.37140
label:0= spoof,1= bonafide- LLR =
score_bonafide − score_spoof
metrics.json
Per-run aggregated metrics (EER, ACC, F1, confidence intervals).
bias_fairness/.../utterances.txt
Per-utterance scores with subgroup columns (gender, accent, NISQA quality, etc.).
Score columns: score_spoof, score_bonafide (renamed from legacy class0/class1).