@inproceedings{le-2026-pogranichnyzashchitnik,
title = "{P}ogranichny{Z}ashchitnik at {A}r{A}-{DF} 2026: {W}av{LM} Large with {L}o{RA} and One-Class Softmax for {A}rabic Speech Deepfake Detection",
author = "Le, Minh-Hoang",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.191/",
pages = "1491--1495",
ISBN = "979-8-89176-497-2",
abstract = "This paper describes the system submitted by team $\textbf{PogranichnyZashchitnik}$ to the ArA-DF 2026 shared task on Arabic Audio Deepfake Detection. We build on $\textbf{WavLM Large}$ as a self-supervised feature extractor, fine-tuned with $\textbf{Low-Rank Adaptation (LoRA)}$ so that only a small fraction of parameters need updating. Representations from all 25 hidden layers are blended via a $\textbf{learnable weighted sum}$, pooled into utterance-level embeddings with $\textbf{Attentive Statistics Pooling}$, and classified by an $\textbf{OC-Softmax}$ head that treats bonafide speech as the target class. Combined with an augmentation pipeline of codec compression, additive noise, and reverberation, this single system achieves 12.54{\%} EER on Track 1 (Dialect Generalization, 14th/15) and 5.66{\%} EER on Track 2 (Acoustic Robustness, 8th/14), outperforming the organizer baseline on both tracks. Post-evaluation analysis shows that utterances shorter than two seconds are disproportionately difficult, and that a non-trivial fraction of spoofed samples are scored as confidently bonafide under dialect mismatch."
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<abstract>This paper describes the system submitted by team PogranichnyZashchitnik to the ArA-DF 2026 shared task on Arabic Audio Deepfake Detection. We build on WavLM Large as a self-supervised feature extractor, fine-tuned with Low-Rank Adaptation (LoRA) so that only a small fraction of parameters need updating. Representations from all 25 hidden layers are blended via a learnable weighted sum, pooled into utterance-level embeddings with Attentive Statistics Pooling, and classified by an OC-Softmax head that treats bonafide speech as the target class. Combined with an augmentation pipeline of codec compression, additive noise, and reverberation, this single system achieves 12.54% EER on Track 1 (Dialect Generalization, 14th/15) and 5.66% EER on Track 2 (Acoustic Robustness, 8th/14), outperforming the organizer baseline on both tracks. Post-evaluation analysis shows that utterances shorter than two seconds are disproportionately difficult, and that a non-trivial fraction of spoofed samples are scored as confidently bonafide under dialect mismatch.</abstract>
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%0 Conference Proceedings
%T PogranichnyZashchitnik at ArA-DF 2026: WavLM Large with LoRA and One-Class Softmax for Arabic Speech Deepfake Detection
%A Le, Minh-Hoang
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F le-2026-pogranichnyzashchitnik
%X This paper describes the system submitted by team PogranichnyZashchitnik to the ArA-DF 2026 shared task on Arabic Audio Deepfake Detection. We build on WavLM Large as a self-supervised feature extractor, fine-tuned with Low-Rank Adaptation (LoRA) so that only a small fraction of parameters need updating. Representations from all 25 hidden layers are blended via a learnable weighted sum, pooled into utterance-level embeddings with Attentive Statistics Pooling, and classified by an OC-Softmax head that treats bonafide speech as the target class. Combined with an augmentation pipeline of codec compression, additive noise, and reverberation, this single system achieves 12.54% EER on Track 1 (Dialect Generalization, 14th/15) and 5.66% EER on Track 2 (Acoustic Robustness, 8th/14), outperforming the organizer baseline on both tracks. Post-evaluation analysis shows that utterances shorter than two seconds are disproportionately difficult, and that a non-trivial fraction of spoofed samples are scored as confidently bonafide under dialect mismatch.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.191/
%P 1491-1495
Markdown (Informal)
[PogranichnyZashchitnik at ArA-DF 2026: WavLM Large with LoRA and One-Class Softmax for Arabic Speech Deepfake Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.191/) (Le, ArabicNLP 2026)
ACL