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.

通过使用波动的信号采样率和通过动态声音触发器（例如拍手）掩盖说话者身份，DynamicTrigger 被引入为一种执行动态后门攻击的方法，从而确保已损坏的样本与干净的样本无法区分，通过实证测试，证明了 DynamicTrigger 在隐蔽攻击中取得了令人印象深刻的成功率，并在非受损数据集上保持了出色的准确性。