# Stealthy Backdoor Attack via Confidence-driven Sampling

arXiv: https://arxiv.org/abs/2310.05263 · HTML: https://mlsanity.com/paper/2310.05263/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2023.

## Built on by (most cited first, 1 of 1)

- [Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks](https://mlsanity.com/paper/2608.09047/) (2026)

## Abstract (arXiv)

Backdoor attacks aim to surreptitiously insert malicious triggers into DNN models, granting unauthorized control during testing scenarios. Existing methods lack robustness against defense strategies and predominantly focus on enhancing trigger stealthiness while randomly selecting poisoned samples. Our research highlights the overlooked drawbacks of random sampling, which make that attack detectable and defensible. The core idea of this paper is to strategically poison samples near the model's decision boundary and increase defense difficulty. We introduce a straightforward yet highly effective sampling methodology that leverages confidence scores. Specifically, it selects samples with lower confidence scores, significantly increasing the challenge for defenders in identifying and countering these attacks. Importantly, our method operates independently of existing trigger designs, providing versatility and compatibility with various backdoor attack techniques. We substantiate the effectiveness of our approach through a comprehensive set of empirical experiments, demonstrating its potential to significantly enhance resilience against backdoor attacks in DNNs.