# UPSET and ANGRI : Breaking High Performance Image Classifiers

arXiv: https://arxiv.org/abs/1707.01159 · HTML: https://mlsanity.com/paper/1707.01159/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2017.

## Built on by (most cited first, 19 of 19)

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- [Security and Privacy for Artificial Intelligence: Opportunities and Challenges](https://mlsanity.com/paper/2102.04661/) (2021)
- [Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective](https://mlsanity.com/paper/2101.03042/) (2021)
- [A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking](https://mlsanity.com/paper/2306.12111/) (2023)
- [A Survey of Neural Network Robustness Assessment in Image Recognition](https://mlsanity.com/paper/2404.08285/) (2024)
- [Surround-View Cameras based Holistic Visual Perception for Automated Driving](https://mlsanity.com/paper/2206.05542/) (2022)
- [Sparse and Transferable Universal Singular Vectors Attack](https://mlsanity.com/paper/2401.14031/) (2024)
- [Adversarial Attacks with Time-Scale Representations](https://mlsanity.com/paper/2107.12473/) (2021)
- [Towards Robust Stacked Capsule Autoencoder with Hybrid Adversarial Training](https://mlsanity.com/paper/2202.13755/) (2022)
- [CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception](https://mlsanity.com/paper/2309.01102/) (2023)
- [Optimistic Gradient Learning with Hessian Corrections for High-Dimensional Black-Box Optimization](https://mlsanity.com/paper/2502.04829/) (2025)
- [Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecasting](https://mlsanity.com/paper/2512.12154/) (2025)
- [Operational Robustness of LLMs on Code Generation](https://mlsanity.com/paper/2602.18800/) (2026)

## Abstract (arXiv)

In this paper, targeted fooling of high performance image classifiers is achieved by developing two novel attack methods. The first method generates universal perturbations for target classes and the second generates image specific perturbations. Extensive experiments are conducted on MNIST and CIFAR10 datasets to provide insights about the proposed algorithms and show their effectiveness.