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Enhanced Elimination of Poison

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Poison can be effectively removed from the gastrointestinal (GI) tract through various decontamination procedures.
Antidotes serve a crucial role in counteracting the effects of poison by inhibiting enzymes responsible for producing harmful drug metabolites. In some cases, these toxic metabolites can be neutralized by endogenous cosubstrates, which are maintained at specific concentrations to prevent interaction with cellular macromolecules and subsequent cell death.
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Related Experiment Video

Updated: Jun 12, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Ghost poisoning: Making users invisible to speaker verification models.

Nicholas Mehlman¹, Shrikanth Narayanan¹

¹University of Southern California, Los Angeles, California 90089, USA.

JASA Express Letters

|June 11, 2026

Summary

This summary is machine-generated.

Researchers created "ghost speakers" by poisoning speaker verification models. This method hinders system accuracy for a specific individual, offering robust identity obfuscation for potential misuse.

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Related Experiment Videos

Last Updated: Jun 12, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Area of Science:

Artificial Intelligence
Machine Learning
Biometrics

Background:

Speaker verification systems are crucial for security.
Current models face vulnerabilities to adversarial attacks.
Identity obfuscation techniques are increasingly important.

Purpose of the Study:

To demonstrate the creation of
ghost speakers
using model poisoning.

Main Methods:

A novel model poisoning technique based on contrastive learning was developed.
The method targets specific individuals to degrade system performance.
Three popular speaker verification models were used for testing.

Main Results:

The proposed method significantly increases the equal error rate for the targeted
ghost speaker
by 10-20 times.
Speaker verification accuracy for other users remains largely unaffected.
The technique provides a robust layer of identity obfuscation.

Conclusions:

Speaker verification models are susceptible to ghost speaker attacks.
This vulnerability could be exploited for malicious purposes, highlighting security risks.
Further research is needed to develop defenses against such adversarial attacks.