Deep noise suppression (DNS) models enjoy widespread use throughout a variety of high-stakes speech applications. However, in this paper, we show that four recent DNS models can each be reduced to outputting unintelligible gibberish through the addition of imperceptible adversarial noise. Furthermore, our results show the near-term plausibility of targeted attacks, which could induce models to output arbitrary utterances, and over-the-air attacks. While the success of these attacks varies by model and setting, and attacks appear to be strongest when model-specific (i.e., white-box and non-transferable), our results highlight a pressing need for practical countermeasures in DNS systems.

The rapid proliferation of generative audio synthesis and editing technologies has raised significant concerns about copyright infringement, data provenance, and the spread of misinformation through deepfake audio. Watermarking offers a proactive solution by embedding imperceptible, identifiable, and traceable marks into audio content. While recent neural network-based watermarking methods like WavMark and AudioSeal have improved robustness and quality, they struggle to achieve both robust detection and accurate attribution simultaneously. This paper introduces Cross-Attention Robust Audio Watermark (XAttnMark), which bridges this gap by leveraging partial parameter sharing between the generator and the detector, a cross-attention mechanism for efficient message retrieval, and a temporal conditioning module for improved message distribution. Additionally, we propose a psychoacoustic-aligned temporal-frequency masking loss that captures fine-grained auditory masking effects, enhancing watermark imperceptibility. Our approach achieves state-of-the-art performance in both detection and attribution, demonstrating superior robustness against a wide range of audio transformations, including challenging generative editing with strong editing strength. The project webpage is available at https://liuyixin-louis.github.io/xattnmark/.

The transformer is a widely-used building block in modern neural networks. However, when applied to audio data, the transformer's acausal behaviour, which we term Acausal Attention (AA), has generally limited its application to offline tasks. In this paper we introduce Streaming Attention (SA), which operates causally with fixed latency, and requires lower compute and memory resources than AA to train. Next, we introduce Low Latency Streaming Attention (LLSA), a method which combines multiple SA layers without latency build-up proportional to the layer count. Comparative analysis between AA, SA and LLSA on Automatic Speech Recognition (ASR) and Speech Emotion Recognition (SER) tasks are presented. The results show that causal SA-based networks with fixed latencies of a few seconds (e.g. 1.8 seconds) and LLSA networks with latencies as short as 300 ms can perform comparably with acausal (AA) networks. We conclude that SA and LLSA methods retain many of the benefits of conventional acausal transformers, but with latency characteristics that make them practical to run in real-time streaming applications.