ArA-DF 2026: Findings of the First Shared Task on Arabic Speech Deepfake Detection

Abstract

We present ArA-DF 2026, the first shared task for Arabic speech deepfake detection, held at ArabicNLP 2026. Unlike existing benchmarks that conflate all distribution shift into a single evaluation set, ArA-DF 2026 separates dialect generalization (Track 1: unseen Arabic dialects and speakers) from acoustic robustness (Track 2: codec compression, noise, re-verberation, re-encoding). We release 43 , 500 labeled training utterances spanning MSA and regional dialects, plus blind evaluation sets of 144 , 210 (Track 1) and 127 , 746 (Track 2) utterances, ranking systems by EER. Fourteen teams competed on Track 1 and twelve on Track 2, with eight system description papers submitted. The best submissions cut EER from 14 . 54% to 0 . 87% (Track 1) and from 27 . 11% to 0 . 66% (Track 2). Across independent sub-missions, three findings recur: augmentation and data curation move performance far more than architecture; simpler pooling heads often outperform graph-attention back-ends once augmentation is applied; and external synthetic Arabic speech with mismatched generation artifacts hurts rather than helps. Residual errors concentrate on short utterances and on spoofed audio misclassified as bona fide.