Diffusion models are state-of-the-art deep learning generative models that are trained on the principle of learning forward and backward diffusion processes via the progressive addition of noise and denoising. In this paper, we aim to fool audio-based DNN models, such as those from the Hugging Face framework, primarily those that focus on audio, in particular transformer-based artificial intelligence models, which are powerful machine learning models that save time and achieve results faster and more efficiently. We demonstrate the feasibility of backdoor attacks (called `BacKBayDiffMod`) on audio transformers derived from Hugging Face, a popular framework in the world of artificial intelligence research. The backdoor attack developed in this paper is based on poisoning model training data uniquely by incorporating backdoor diffusion sampling and a Bayesian approach to the distribution of poisoned data.

翻译：扩散模型是最先进的深度学习生成模型，其训练原理是通过逐步添加噪声与去噪过程学习前向与反向扩散过程。本文旨在欺骗基于音频的深度神经网络模型，例如源自Hugging Face框架的模型——特别是专注于音频处理的基于Transformer的人工智能模型，这类强大的机器学习模型能够节省时间并更快速高效地获得结果。我们论证了对源自Hugging Face（人工智能研究领域流行框架）的音频Transformer实施后门攻击（称为`BacKBayDiffMod`）的可行性。本文开发的后门攻击基于独特的训练数据投毒方法，通过融合后门扩散采样技术与贝叶斯方法对投毒数据分布进行建模。