Authors - Carl Kugblenu, Petri Vuorimaa Abstract - Compressed-domain audio steganography poses a critical foren sic challenge in modern VoIP systems, particularly within low-bitrate codecs. Traditional deep learning models often lack interpretability and struggle with low embedding rates. This paper introduces AUSPEX, a lightweight forensic framework ( 170k parameters) optimized for uni versal compressed audio steganalysis. A novel three-channel tensoriza tion strategy is proposed; incorporating raw bits, temporal derivatives, and bit stability to amplify subtle embedding perturbations. A non trainable high-pass residual stream further enhances sensitivity to first and second-order temporal noise. To ensure forensic transparency, a dual level explainability framework integrates intrinsic spatial attention with post-hoc Integrated Gradients, providing bit-level evidence attribution. Experiments demonstrate detection across CNV and PMS algorithms at low embedding rates. AUSPEX advances the field by unifying ef f icient, edge-deployable detection with rigorous human-centric forensic interpretability.
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