LowDDAWP-Net: Low-Resolution Double Deep Audio Waveform Prior Network for Audio Systems Reliability Defence
Kai Chen, Yikun Zhang, Jiasong Wu, Jean-Louis Coatrieux, Yang Chen · IEEE Transactions on Reliability · 2025
Improving the information reliability of the audio system is critical to safeguarding the security of the audio system. Adversarial samples crafted by in-the-wild attackers by introducing perturbations to the audio become a severe threat to the trustworthiness of deep learning-based classifiers. To achieve dynamic defence against audio adversarial sample attacks, a low-resolution double deep audio waveform prior network (LowDDAWP-Net) for audio systems reliability defence is proposed. Specifically, LowDDAWP-Net consists of a noise audio prior extraction module ($\mathbf{DAWP}_{\mathbf{noise}}$), an speech prior extraction module ($\mathbf{DAWP}_{\mathbf{speech}}$), a low-resolution extraction module (LREM), and a voice activity detection module (VADM). The role of the VADM is to automatically detect voice activity signals and silent signals from the audio signal.$\mathbf{DAWP}_{\mathbf{speech}}$and$\mathbf{DAWP}_{\mathbf{noise}}$are encoder–decoders with the same architecture. The encoder extracts the superficial features of the input audio, and the decoder performs temporal fusion to form high-dimensional features and reconstructs them into waveform signals. A LREM is employed to extract low-resolution audio to facilitate the encoder–decoder to perform detail on low-resolution audio and to speed up the recovery of DAWP networks to high resolution. The adversarial samples generated by several diverse attack state-of-the-art on three different datasets and their corresponding benign samples form a novel private dataset. The qualitative and quantitative results of the novel private dataset demonstrate the effectiveness and superiority of LowDDAWP-Net.