The area of Machine Learning as a Service (MLaaS) is experiencing increased implementation due to recent advancements in the AI (Artificial Intelligence) industry. However, this spike has prompted concerns regarding AI defense mechanisms, specifically regarding potential covert attacks from third-party providers that cannot be entirely trusted. Recent research has uncovered that auditory backdoors may use certain modifications as their initiating mechanism. DynamicTrigger is introduced as a methodology for carrying out dynamic backdoor attacks that use cleverly designed tweaks to ensure that corrupted samples are indistinguishable from clean. By utilizing fluctuating signal sampling rates and masking speaker identities through dynamic sound triggers (such as the clapping of hands), it is possible to deceive speech recognition systems (ASR). Our empirical testing demonstrates that DynamicTrigger is both potent and stealthy, achieving impressive success rates during covert attacks while maintaining exceptional accuracy with non-poisoned datasets.

翻译：机器学习即服务（MLaaS）领域因人工智能（AI）产业的近期发展而加速实施。然而，这一增长引发了对AI防御机制的担忧，特别是来自不可完全信任的第三方提供商的潜在隐蔽攻击。近期研究发现，听觉后门可能利用特定修改作为启动机制。本文提出"动态触发器"（DynamicTrigger）方法，通过精心设计的调整执行动态后门攻击，确保被污染样本与干净样本无法区分。通过利用波动信号采样率及动态声音触发（如拍手声）遮蔽说话人身份，可欺骗语音识别系统（ASR）。实证测试表明，DynamicTrigger兼具强效性与隐蔽性，在隐蔽攻击中实现显著成功率，同时在使用非污染数据集时保持卓越准确率。