mlsanityData to 7 Oct 2026

UPSET and ANGRI : Breaking High Performance Image Classifiers

arXiv 1707.01159 ↗First posted 4 Jul 2017cs.CVcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2017.

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Citing papers per year: 2021: 7; 2022: 4; 2023: 3; 2024: 2; 2025: 2; 2026: 1.

19 papers.

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  1. 2021Relating Adversarially Robust Generalization to Flat Minimacited by 57
  2. 2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 51
  3. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  4. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  5. 2021Parameter identifiability of a deep feedforward ReLU neural networkcited by 24
  6. 2022Rank List Sensitivity of Recommender Systems to Interaction Perturbationscited by 20
  7. 2022Adversarially-Aware Robust Object Detectorcited by 20
  8. 2021Security and Privacy for Artificial Intelligence: Opportunities and Challengescited by 16
  9. 2021Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspectivecited by 9
  10. 2023A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarkingcited by 5
  11. 2022Surround-View Cameras based Holistic Visual Perception for Automated Drivingcited by 2
  12. 2024A Survey of Neural Network Robustness Assessment in Image Recognitioncited by 2
7 more
  1. 2024Sparse and Transferable Universal Singular Vectors Attackcited by 1
  2. 2021Adversarial Attacks with Time-Scale Representationscited by 0
  3. 2022Towards Robust Stacked Capsule Autoencoder with Hybrid Adversarial Trainingcited by 0
  4. 2023CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perceptioncited by 0
  5. 2025Optimistic Gradient Learning with Hessian Corrections for High-Dimensional Black-Box Optimizationcited by 0
  6. 2025Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecastingcited by 0
  7. 2026Operational Robustness of LLMs on Code Generationcited by 0