# Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach

arXiv: https://arxiv.org/abs/2406.10719 · HTML: https://mlsanity.com/paper/2406.10719/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 65 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 30 of 30)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Backdoor Learning: A Survey](https://mlsanity.com/paper/2007.08745/) (2020)
- [Seamless: Multilingual Expressive and Streaming Speech Translation](https://mlsanity.com/paper/2312.05187/) (2023)
- [Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses](https://mlsanity.com/paper/2012.10544/) (2020)
- [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://mlsanity.com/paper/2110.01900/) (2021)
- [How to Backdoor Diffusion Models?](https://mlsanity.com/paper/2212.05400/) (2022)
- [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://mlsanity.com/paper/2101.07597/) (2021)
- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models](https://mlsanity.com/paper/2306.06874/) (2023)
- [Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits](https://mlsanity.com/paper/2102.10496/) (2021)
- [Stealthy Backdoor Attack for Code Models](https://mlsanity.com/paper/2301.02496/) (2023)
- [Can You Hear It? Backdoor Attacks via Ultrasonic Triggers](https://mlsanity.com/paper/2107.14569/) (2021)
- [The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipeline](https://mlsanity.com/paper/2401.04136/) (2024)
- [Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift](https://mlsanity.com/paper/2312.00050/) (2023)
- [BackdoorBox: A Python Toolbox for Backdoor Learning](https://mlsanity.com/paper/2302.01762/) (2023)
- [BayBFed: Bayesian Backdoor Defense for Federated Learning](https://mlsanity.com/paper/2301.09508/) (2023)
- [Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2302.14564/) (2023)
- [Towards Unified Robustness Against Both Backdoor and Adversarial Attacks](https://mlsanity.com/paper/2405.17929/) (2024)
- [Adversarial Machine Learning: Bayesian Perspectives](https://mlsanity.com/paper/2003.03546/) (2020)
- [Bayesian optimization in ab initio nuclear physics](https://mlsanity.com/paper/1902.00941/) (2019)
- [Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data](https://mlsanity.com/paper/2310.06372/) (2023)
- [A Survey on Poisoning Attacks Against Supervised Machine Learning](https://mlsanity.com/paper/2202.02510/) (2022)
- [Dictionary Attacks on Speaker Verification](https://mlsanity.com/paper/2204.11304/) (2022)
- [Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution](https://mlsanity.com/paper/2205.09167/) (2022)
- [The last Dance : Robust backdoor attack via diffusion models and bayesian approach](https://mlsanity.com/paper/2402.05967/) (2024)
- [Impulse Control of a Diffusion with a Change Point](https://mlsanity.com/paper/1404.1761/) (2014)

## Built on by (most cited first, 3 of 3)

- [Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models](https://mlsanity.com/paper/2506.11521/) (2025)
- [Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization](https://mlsanity.com/paper/2407.14573/) (2024)
- [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024)

## Abstract (arXiv)

With the growing use of voice-activated systems and speech recognition technologies, the danger of backdoor attacks on audio data has grown significantly. This research looks at a specific type of attack, known as a Stochastic investment-based backdoor attack (MarketBack), in which adversaries strategically manipulate the stylistic properties of audio to fool speech recognition systems. The security and integrity of machine learning models are seriously threatened by backdoor attacks, in order to maintain the reliability of audio applications and systems, the identification of such attacks becomes crucial in the context of audio data. Experimental results demonstrated that MarketBack is feasible to achieve an average attack success rate close to 100% in seven victim models when poisoning less than 1% of the training data.