# CLIP-guided Diffusion Model for Backdoor Generation in Sensor-based Human Activity Recognition

arXiv: https://arxiv.org/abs/2606.22837 · HTML: https://mlsanity.com/paper/2606.22837/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security.
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Placed in

- LLM safety and security / [Backdoor and data poisoning attacks](https://mlsanity.com/safety/backdoor-and-data-poisoning-attacks/)

## Builds on (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021)
- [Hidden Trigger Backdoor Attacks](https://mlsanity.com/paper/1910.00033/) (2019)
- [A Survey on Generative Diffusion Model](https://mlsanity.com/paper/2209.02646/) (2022)
- [Poisoning and Backdooring Contrastive Learning](https://mlsanity.com/paper/2106.09667/) (2021)
- [How to Backdoor Diffusion Models?](https://mlsanity.com/paper/2212.05400/) (2022)
- [Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy](https://mlsanity.com/paper/2101.09878/) (2021)
- [PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments](https://mlsanity.com/paper/2501.15563/) (2025)
- [Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework](https://mlsanity.com/paper/2508.03989/) (2025)
- [Dynamic Black-box Backdoor Attacks on IoT Sensory Data](https://mlsanity.com/paper/2511.14074/) (2025)
- [Log Anomaly Detection with Large Language Models via Knowledge-Enriched Fusion](https://mlsanity.com/paper/2512.11997/) (2025)
- [Contrastive Continual Learning for Model Adaptability in Internet of Things](https://mlsanity.com/paper/2602.04881/) (2026)

## Abstract (arXiv)

Sensors are critical components of modern intelligent devices. The proliferation of the Internet of Things (IoT) and wearable mobile devices has enabled the integration of such sensors to monitor the environment and enable users to take predictive actions. Human activity recognition (HAR) is a popular application in which Inertial Measurement Unit (IMU)-based sensors, such as accelerometers and gyroscopes, are used to provide insights into health, training, and medical diagnosis. However, the accuracy of such a model is hindered by the lack of data. The diffusion model-based technique has proven successful in generating synthetic data for training HAR models. In this paper, we propose a backdoor training technique, IMU-DM-CLIP, that leverages a diffusion model to enable trigger-based attacks on HAR models. Our empirical analysis shows that the attack is successful even with a very small backdoor injection rate of 10% and 10% of the data guided for the diffusion model.