mlsanityData to 7 Oct 2026

Unadversarial Examples: Designing Objects for Robust Vision

arXiv 2012.12235 ↗First posted 22 Dec 2020cs.CV cs.LGcited by 45

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Abstract (arXiv)

We study a class of realistic computer vision settings wherein one can influence the design of the objects being recognized. We develop a framework that leverages this capability to significantly improve vision models' performance and robustness. This framework exploits the sensitivity of modern machine learning algorithms to input perturbations in order to design "robust objects," i.e., objects that are explicitly optimized to be confidently detected or classified. We demonstrate the efficacy of the framework on a wide variety of vision-based tasks ranging from standard benchmarks, to (in-simulation) robotics, to real-world experiments. Our code can be found at https://git.io/unadversarial .

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2020.

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Citing papers per year: 2021: 7; 2022: 15; 2023: 10; 2024: 8; 2025: 2; 2026: 3.

45 papers.

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  1. 2022Exploring Visual Prompts for Adapting Large-Scale Modelscited by 271
  2. 2022Truth Serum: Poisoning Machine Learning Models to Reveal Their Secretscited by 94
  3. 2022Understanding and Improving Visual Prompting: A Label-Mapping Perspectivecited by 86
  4. 2021Improved OOD Generalization via Adversarial Training and Pre-trainingcited by 66
  5. 2021Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Trainingcited by 58
  6. 2022Visual Prompting for Adversarial Robustnesscited by 33
  7. 2022A Survey on Physical Adversarial Attack in Computer Visioncited by 24
  8. 2022The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Trainingcited by 22
  9. 2022Defensive Patches for Robust Recognition in the Physical Worldcited by 20
  10. 2021"Adversarial Examples" for Proof-of-Learningcited by 19
  11. 2023The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attackscited by 15
  12. 2022Evolution of Neural Tangent Kernels under Benign and Adversarial Trainingcited by 14
33 more
  1. 2023Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyondcited by 14
  2. 2022Can Adversarial Training Be Manipulated By Non-Robust Features?cited by 12
  3. 2024Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalizationcited by 12
  4. 2021Simulated Adversarial Testing of Face Recognition Modelscited by 10
  5. 2022Squeeze Training for Adversarial Robustnesscited by 10
  6. 2023Adversarial Examples in the Physical World: A Surveycited by 10
  7. 2022Task Bias in Vision-Language Modelscited by 8
  8. 2022Breaking Correlation Shift via Conditional Invariant Regularizercited by 7
  9. 2022Physical Passive Patch Adversarial Attacks on Visual Odometry Systemscited by 6
  10. 2025Generalizing vision-language models to novel domains: A comprehensive surveycited by 6
  11. 2023Adversarial Machine Learning for Social Good: Reframing the Adversary as an Allycited by 5
  12. 2024Towards Adversarial Robustness via Debiased High-Confidence Logit Alignmentcited by 5
  13. 2021Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attackscited by 4
  14. 2021Amicable Aid: Perturbing Images to Improve Classification Performancecited by 3
  15. 2023Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Indexcited by 3
  16. 2023A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applicationscited by 3
  17. 2023Uncovering the Hidden Cost of Model Compressioncited by 3
  18. 2024Proactive Schemes: A Survey of Adversarial Attacks for Social Goodcited by 2
  19. 2024L-WISE: Boosting Human Visual Category Learning Through Model-Based Image Selection and Enhancementcited by 2
  20. 2025ALMGuard: Safety Shortcuts and Where to Find Them as Guardrails for Audio-Language Modelscited by 2
  21. 2023NSA: Naturalistic Support Artifact to Boost Network Confidencecited by 1
  22. 2024On the low-shot transferability of [V]-Mambacited by 1
  23. 2024Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Datacited by 1
  24. 2021Improving Robustness with Image Filteringcited by 0
  25. 2022Adversarial Attack Against Image-Based Localization Neural Networkscited by 0
  26. 2022Building Resilience to Out-of-Distribution Visual Data via Input Optimization and Model Finetuningcited by 0
  27. 2023Area is all you need: repeatable elements make stronger adversarial attackscited by 0
  28. 2023Universal Perturbation-based Secret Key-Controlled Data Hidingcited by 0
  29. 2024Redesigning Traffic Signs to Mitigate Machine-Learning Patch Attackscited by 0
  30. 2024RED: Robust Environmental Designcited by 0
  31. 2026Rectifying Adversarial Examples Using Their Vulnerabilitiescited by 0
  32. 2026Adjustable Text-Guided Backdoor Attacks with Natural-Word Triggers on Multimodal Pretrained Modelscited by 0
  33. 2026Adaptive Causal Alignment for High-Confidence Adversarial Trainingcited by 0