We describe our submission to ArA-DF 2026, the ArabicNLP shared task on Arabic speech deepfake detection, in which systems classify audio as real or deepfake. We participated in both tracks: Track 1 (Dialect Generalization) and Track 2 (Acoustic Robustness). Our systems investigated finetuning three large self-supervised speech encoders — WavLM, wav2vec2-XLS-R, and HuBERT — with a learnable attentive-pooling head. We evaluated each encoder separately and further combined encoders through a logistic-regression meta-classifier. In addition to improve robustness we apply an acoustic degradation pipeline (additive noise, lossy codec re-encoding, band-limiting, and gain) to 50% of the training data. For track one the best achieved result is, a fully fine-tuned WavLM with attentive pooling and augmentation, which scored 2.19% EER test set, and a deepfake-pretrained XLS-R achieves 7.80% EER on Track2 which out performed the official baseline. Our ablation study shows that augmentation and backbone choice are the primary factors, while adding correlated ensemble members yields no gain.