This paper presents the SparkShield system submitted to ArA-DF 2026 Track 2, which focuses on robust Arabic speech deepfake detection under realistic acoustic degradations. To improve robustness, we apply a comprehensive augmentation pipeline involving background noise, reverberation, codec compression, reencoding, and volume perturbation. Our system consists of three parallel subsystems using XLS-R models with 300M, 1B, and 2B parameters as front-end feature extractors. Each subsystem employs learnable layer-wise feature fusion, followed by a Multi-kernel gated Convolution (MultiConv) module and Multi-Head Attention Pooling (MHAP) for utterance-level spoof detection. The output logits of the three independently trained subsystems are finally fused. The proposed system achieves an EER of 0.6575% on the evaluation set and ranks first in ArA-DF 2026 Track 2.