Redirecting to original paper in 30 seconds...

Click below to go immediately or wait for automatic redirect

arxiv_ml 95% Match Research Paper AI security researchers,Speech technology developers,Machine learning engineers,Cybersecurity professionals 2 weeks ago

Are Modern Speech Enhancement Systems Vulnerable to Adversarial Attacks?

ai-safety › robustness
📄 Abstract

Abstract: Machine learning approaches for speech enhancement are becoming increasingly expressive, enabling ever more powerful modifications of input signals. In this paper, we demonstrate that this expressiveness introduces a vulnerability: advanced speech enhancement models can be susceptible to adversarial attacks. Specifically, we show that adversarial noise, carefully crafted and psychoacoustically masked by the original input, can be injected such that the enhanced speech output conveys an entirely different semantic meaning. We experimentally verify that contemporary predictive speech enhancement models can indeed be manipulated in this way. Furthermore, we highlight that diffusion models with stochastic samplers exhibit inherent robustness to such adversarial attacks by design.
Authors (3)
Rostislav Makarov
Lea Schönherr
Timo Gerkmann
Submitted
September 25, 2025
arXiv Category
eess.AS
arXiv PDF

Key Contributions

This paper demonstrates that modern speech enhancement systems are vulnerable to adversarial attacks, where carefully crafted noise can alter the semantic meaning of the enhanced speech. It also highlights that diffusion models with stochastic samplers exhibit inherent robustness against such attacks.

Business Value

Crucial for building secure and trustworthy speech technologies, preventing malicious manipulation of audio content and ensuring the reliability of voice interfaces and communication systems.

Paper Metadata

Innovation Type

Analysis/Finding

Deployment Feasibility

High for analysis. Deployment of robust systems requires careful model selection and potentially defense mechanisms.

Limitations Addressed

Addresses the potential security risks and lack of robustness in advanced speech enhancement models, which could be exploited to manipulate communication.

Technical Tags

speech enhancementadversarial attackssemantic meaningpsychoacoustic maskingdiffusion modelsstochastic samplersvulnerabilityrobustnessmachine learning securityaudio processing

Research Topics

Adversarial Machine LearningSpeech ProcessingAI SecurityRobustness of Deep Learning ModelsGenerative Models

Methods & Architectures

Adversarial Attack CraftingPsychoacoustic MaskingExperimental VerificationDiffusion Model Analysis Predictive Speech Enhancement ModelsDiffusion Models

Applications & Tasks

Audio Processing Speech Technology AI Security Cybersecurity Model VulnerabilityRobustness TestingSecurity Analysis Investigating the susceptibility of speech enhancement systems to adversarial attacks that alter semantic meaning.

Related Fields

Computer SecurityAudio Signal ProcessingMachine LearningCybersecurity

Keywords

speech enhancementadversarial attackssemantic meaningpsychoacoustic maskingdiffusion modelsrobustnessvulnerabilityAI securityaudio processingdeep learningmachine learning security

Academic Context

#Adversarial Machine Learning#Speech Processing#AI Security#Robustness of Deep Learning Models#Generative Models

Commercial Potential

Potential Products
Secure speech enhancement modulesRobust voice assistantsAudio content verification tools
Target Industries
TechnologyTelecommunicationsAutomotive (in-car assistants)Security
Use Case Examples
Ensuring voice commands to smart devices cannot be maliciously altered.Preventing manipulation of recorded evidence.Securing real-time communication systems against semantic hijacking.
Competitive Edge
Highlights a critical security flaw in current speech enhancement models and points towards diffusion models as a more robust alternative.
Market Opportunity
Large and growing market for speech technology and AI security.
Revenue Models
Security consultinglicensing of robust speech modelsdevelopment of secure audio processing tools.

Resource Requirements

Compute Needs
Moderate for running attacks and evaluating models.
Data Requirements
Requires speech datasets and trained speech enhancement models.
Deployment Constraints
Implementing defenses against adversarial attacks can increase computational cost and complexity.
Scalability
The vulnerability exists across various model sizes; robustness of diffusion models is a key finding.
Regulatory Considerations
Potential implications for regulations concerning audio manipulation and deepfakes.

Production Readiness

Maturity Level

Research/Analysis

Time to Market

Short for awareness, Medium for implementing robust solutions.

Patent Potential

Moderate, for defense mechanisms against adversarial attacks on speech systems.

View Full Paper Back to Papers