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The paper introduces HEAR, a large-scale dataset for Acoustic Side-Channel Attacks (ASCA) on keyboards, designed to study generalization across users, keyboards, and environments. To address the challenge of domain shift in ASCA, they propose DECKER, a domain-invariant keystroke inference framework that uses keyboard signature normalization, domain-adversarial disentanglement, cross-keyboard contrastive alignment, and acoustic style randomization. Experiments on HEAR demonstrate that DECKER improves keystroke identification, especially in cross-keyboard and cross-user settings, highlighting the persistent security risk of ASCA.
Even with domain adaptation, your keystrokes are still vulnerable to acoustic side-channel attacks across diverse keyboards, users, and noisy environments.
Acoustic side-channel attacks (ASCA) on keyboards pose a significant security risk, as keystrokes can be inferred from typing acoustics, revealing sensitive information. Prior ASCA studies are limited by small-scale datasets with restricted diversity in users, keyboards, and environments, constraining analysis across devices, microphones, and noise conditions. We introduce HEAR, a dataset designed to study ASCA along three axes: keyboard generalization, noise adaptation, and user bias. HEAR contains recordings from 53 participants using 37 laptop keyboards, collected in three realistic settings: (1) external microphone capture, (2) device microphone capture without network noise, and (3) VoIP-based streaming capture. This enables controlled evaluation across users, keyboards, and environments. On HEAR, we establish an ASCA benchmark spanning conventional features and pre-trained representations from raw audio and spectrograms in unimodal and multimodal settings. We propose DECKER, a domain-invariant keystroke inference framework with four stages: (1) Keyboard Signature Normalization to reduce device coloration, (2) domain-adversarial disentanglement to suppress keyboard identity, (3) supervised cross-keyboard contrastive alignment to enforce key consistency, and (4) Acoustic Style Randomization to synthesize unseen keyboard responses. We further explore sentence-level inference using an LLM-based post-processing layer to refine keystroke sequences via linguistic context. Results on HEAR show DECKER improves keystroke identification over strong baselines, particularly in cross-keyboard and cross-user settings, with further gains from language-model rectification. These findings highlight that ASCA remains effective across diverse users, devices, and noisy environments, underscoring its practical security risk.
