  The task of partially spoofed audio localization aims to accurately determine
audio authenticity at a frame level. Although some works have achieved
encouraging results, utilizing boundary information within a single model
remains an unexplored research topic. In this work, we propose a novel method
called Boundary-aware Attention Mechanism (BAM). Specifically, it consists of
two core modules: Boundary Enhancement and Boundary Frame-wise Attention. The
former assembles the intra-frame and inter-frame information to extract
discriminative boundary features that are subsequently used for boundary
position detection and authenticity decision, while the latter leverages
boundary prediction results to explicitly control the feature interaction
between frames, which achieves effective discrimination between real and fake
frames. Experimental results on PartialSpoof database demonstrate our proposed
method achieves the best performance. The code is available at
https://github.com/media-sec-lab/BAM.
