最后一舞：基于扩散模型与贝叶斯方法的稳健后门攻击
The last Dance : Robust backdoor attack via diffusion models and bayesian approach
AI总结：
本文提出一种名为BacKBayDiffMod的后门攻击方法，利用扩散模型和贝叶斯方法投毒音频Transformer的训练数据，实现对Hugging Face音频模型的有效攻击。
摘要：
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和去噪来学习前向与反向扩散过程。在本文中，我们旨在欺骗基于音频的深度神经网络模型，例如来自Hugging Face框架的模型，尤其是那些专注于音频的基于Transformer的人工智能模型，这些模型是强大的机器学习模型，能够节省时间并更快、更高效地取得结果。我们证明了在源自Hugging Face（人工智能研究领域中的流行框架）的音频Transformer上实施后门攻击（称为“BacKBayDiffMod”）的可行性。本文开发的后门攻击基于通过结合后门扩散采样和贝叶斯方法对中毒数据分布进行独特地投毒模型训练数据。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
