Summary of The Last Dance : Robust Backdoor Attack Via Diffusion Models and Bayesian Approach, by Orson Mengara
The last Dance : Robust backdoor attack via diffusion models and bayesian approach
by Orson Mengara
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel attack technique, BacKBayDiffMod, which aims to deceive audio-based deep neural networks (DNNs) from Hugging Face, a prominent framework in AI research. The authors leverage diffusion models, state-of-the-art generative models that learn forward and backward processes by adding noise and denoising, to create backdoor attacks on transformer-based AI models. By injecting poisoned data into the training process using Bayesian methods and backdoor diffusion sampling, the attackers can manipulate the model’s behavior, compromising its ability to accurately classify audio inputs. The feasibility of this attack is demonstrated on Hugging Face-based audio transformers, highlighting the importance of robustness in AI model development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to trick artificial intelligence (AI) models that are used for audio processing, like speech recognition or music analysis. This paper shows how to create a “backdoor” attack that can deceive these AI models by manipulating their training data. The authors use a type of machine learning called diffusion models, which generate new data by adding noise and then removing it. They combine this technique with Bayesian methods to poison the AI model’s training data, making it misclassify audio inputs. This demonstrates how vulnerable some AI models can be and why we need to develop more robust models. |
Keywords
* Artificial intelligence * Diffusion * Machine learning * Transformer
