DeepFense/Studies_Scores_Parquet

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).