Abstract
Current speech deepfake detectors treat each utterance as a flat sequence of frames, limiting their ability to capture long-range spectral inconsistencies that span non-adjacent regions of an audio signal. We propose \textbf{GraphSeal}, which constructs a temporal-spectral graph over short speech segments and propagates artifact cues through graph attention layers before a global readout produces the bonafide-vs-spoof decision. Each node in the graph corresponds to a fixed-length segment; edges encode temporal adjacency and spectral similarity, letting the network attend to distant frames that share suspicious spectral patterns. A multi-head graph attention mechanism weights neighboring nodes according to both learned features and edge attributes, and a hierarchical readout aggregates node representations into a single utterance-level embedding for binary classification. GraphSeal is front-end agnostic: it operates on top of any self-supervised or handcrafted feature extractor. On ASVspoof 2019~LA, ASVspoof 2021~LA, ASVspoof~5, and the In-the-Wild dataset, GraphSeal achieves equal error rates of 0.52\%, 2.41\%, 8.17\%, and 5.24\%, respectively, matching or outperforming prior methods that rely on fixed-topology graph networks or purely sequential architectures.
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URL
https://arxiv.org/abs/2611.06171
