Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach

ArXiv ID: 2406.10719 “View on arXiv”

Authors: Unknown

Abstract

With the growing use of voice-activated systems and speech recognition technologies, the danger of backdoor attacks on audio data has grown significantly. This research looks at a specific type of attack, known as a Stochastic investment-based backdoor attack (MarketBack), in which adversaries strategically manipulate the stylistic properties of audio to fool speech recognition systems. The security and integrity of machine learning models are seriously threatened by backdoor attacks, in order to maintain the reliability of audio applications and systems, the identification of such attacks becomes crucial in the context of audio data. Experimental results demonstrated that MarketBack is feasible to achieve an average attack success rate close to 100% in seven victim models when poisoning less than 1% of the training data.

Keywords: Backdoor Attacks, Audio Data, Speech Recognition, Steganography, Adversarial Attacks

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced stochastic financial models (Vasiček, Hull-White, Longstaff-Schwartz) and Bayesian diffusion processes, indicating high mathematical complexity. However, it focuses on a theoretical attack methodology for speech recognition systems with no backtesting on financial data, resulting in low empirical rigor.

  flowchart TD
    A["Research Goal: Develop robust backdoor attack for audio models"] --> B["Methodology: Stochastic Investment & Bayesian Approach"]
    B --> C["Data: Audio Datasets & Victim Models"]
    C --> D["Process: Inject stealthy triggers via stylistic manipulation"]
    D --> E["Process: Bayesian optimization for trigger placement"]
    E --> F["Outcome: High attack success ~100% with <1% data poisoning"]
    F --> G["Outcome: Bypasses detection in 7 speech recognition models"]