This paper presents our submission to the ArADF 2026 Shared Task on Arabic Audio Deepfake Detection, focusing on Track 1, which evaluates robustness to previously unseen Arabic dialects. Our proposed system combines two complementary self-supervised speech encoders, XLS-R-300M and WavLM-Large, each integrated with an AASIST graph-attention back-end for spoofing detection. The final prediction is obtained through score-level fusion of the two independently trained models, enabling the system to leverage the complementary representations learned by each encoder. To improve robustness to domain shift, we introduce two complementary adaptation strategies: telephony channel augmentation to reduce the mismatch between the training and evaluation domains, and an iterative pseudolabel self-training framework that exploits highconfidence predictions on unlabeled evaluation data for progressive model refinement. On the official ArA-DF 2026 Track 1 evaluation set, our system achieved an Equal Error Rate (EER) of 0.9265%, ranking 4th on the official leaderboard. These results demonstrate that combining complementary self-supervised speech representations with domain-aware adaptation techniques provides an effective and robust solution for cross-dialect Arabic audio deepfake detection.