New algorithm for real time voice camouflage

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SUMMARY

The discussion centers on a novel algorithm for real-time voice camouflage designed to thwart eavesdropping by automatic speech recognition systems. This method utilizes predictive attacks to effectively jam the DeepSpeech system, achieving a 3.9x improvement in word error rate and a 6.6x improvement in character error rate compared to baseline methods. The approach is validated in realistic environments, demonstrating practical effectiveness over physical distances. For further details, refer to the research paper and audio samples provided.

PREREQUISITES
  • Understanding of automatic speech recognition systems
  • Familiarity with adversarial attack methodologies
  • Knowledge of real-time signal processing techniques
  • Basic concepts of machine learning and neural networks
NEXT STEPS
  • Research "predictive attacks in machine learning" for advanced techniques
  • Explore "DeepSpeech architecture and performance metrics" for deeper insights
  • Investigate "real-time signal processing algorithms" for practical applications
  • Examine "adversarial machine learning" to understand countermeasures
USEFUL FOR

This discussion is beneficial for researchers in machine learning, cybersecurity professionals focused on eavesdropping prevention, and developers working on speech recognition technologies.

Oldman too
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An interesting approach to those eavesdropping devices in your life.
https://arxiv.org/pdf/2112.07076.pdf

ABSTRACT
Automatic speech recognition systems have created exciting possibilities for
applications, however they also enable opportunities for systematic eavesdropping.
We propose a method to camouflage a person’s voice over-the-air from these
systems without inconveniencing the conversation between people in the room.
standard adversarial attacks are not effective in real-time streaming situations because
the characteristics of the signal will have changed by the time the attack is
executed. We introduce predictive attacks, which achieve real-time performance by
forecasting the attack that will be the most effective in the future. Under real-time
constraints, our method jams the established speech recognition system Deep-
Speech 3.9x more than baselines as measured through word error rate, and 6.6x
more as measured through character error rate. We furthermore demonstrate our
approach is practically effective in realistic environments over physical distances.
 
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