# Towards Class-Oriented Poisoning Attacks Against Neural Networks

arXiv: https://arxiv.org/abs/2008.00047 · HTML: https://mlsanity.com/paper/2008.00047/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Data Poisoning in Deep Learning: A Survey](https://mlsanity.com/paper/2503.22759/) (2025)
- [A Survey of Neural Trojan Attacks and Defenses in Deep Learning](https://mlsanity.com/paper/2202.07183/) (2022)
- [Preference Poisoning Attacks on Reward Model Learning](https://mlsanity.com/paper/2402.01920/) (2024)
- [UIBDiffusion: Universal Imperceptible Backdoor Attack for Diffusion Models](https://mlsanity.com/paper/2412.11441/) (2024)
- [Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment](https://mlsanity.com/paper/2411.15673/) (2024)
- [DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data](https://mlsanity.com/paper/2503.21305/) (2025)
- [Dropout Attacks](https://mlsanity.com/paper/2309.01614/) (2023)
- [SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions](https://mlsanity.com/paper/2605.12792/) (2026)

## Abstract (arXiv)

Poisoning attacks on machine learning systems compromise the model performance by deliberately injecting malicious samples in the training dataset to influence the training process. Prior works focus on either availability attacks (i.e., lowering the overall model accuracy) or integrity attacks (i.e., enabling specific instance-based backdoor). In this paper, we advance the adversarial objectives of the availability attacks to a per-class basis, which we refer to as class-oriented poisoning attacks. We demonstrate that the proposed attack is capable of forcing the corrupted model to predict in two specific ways: (i) classify unseen new images to a targeted "supplanter" class, and (ii) misclassify images from a "victim" class while maintaining the classification accuracy on other non-victim classes. To maximize the adversarial effect as well as reduce the computational complexity of poisoned data generation, we propose a gradient-based framework that crafts poisoning images with carefully manipulated feature information for each scenario. Using newly defined metrics at the class level, we demonstrate the effectiveness of the proposed class-oriented poisoning attacks on various models (e.g., LeNet-5, Vgg-9, and ResNet-50) over a wide range of datasets (e.g., MNIST, CIFAR-10, and ImageNet-ILSVRC2012) in an end-to-end training setting.