mlsanityData to 7 Oct 2026

Synthesizing Robust Adversarial Examples

arXiv 1707.07397 ↗First posted 24 Jul 2017cs.CVcited by 39

Not on the map. Why?

Abstract (arXiv)

Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of robust 3D adversarial objects, and we present the first algorithm for synthesizing examples that are adversarial over a chosen distribution of transformations. We synthesize two-dimensional adversarial images that are robust to noise, distortion, and affine transformation. We apply our algorithm to complex three-dimensional objects, using 3D-printing to manufacture the first physical adversarial objects. Our results demonstrate the existence of 3D adversarial objects in the physical world.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jul 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 8; 2023: 6; 2024: 8; 2025: 5; 2026: 8.

39 papers.

Sort:

By area:

  1. 2023Raising the Cost of Malicious AI-Powered Image Editingcited by 160
  2. 2022Narcissus: A Practical Clean-Label Backdoor Attack with Limited Informationcited by 102
  3. 2021WaveGuard: Understanding and Mitigating Audio Adversarial Examplescited by 39
  4. 2023Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compressioncited by 39
  5. 2024Copyright Protection in Generative AI: A Technical Perspectivecited by 35
  6. 2022A Survey on Physical Adversarial Attack in Computer Visioncited by 24
  7. 2022Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantificationcited by 16
  8. 2022Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculumcited by 15
  9. 2022Do Perceptually Aligned Gradients Imply Adversarial Robustness?cited by 15
  10. 2022Demystifying the Adversarial Robustness of Random Transformation Defensescited by 14
  11. 2024Object Detectors in the Open Environment: Challenges, Solutions, and Outlookcited by 13
  12. 2023Targeted Adversarial Attacks on Generalizable Neural Radiance Fieldscited by 10
27 more
  1. 2024PRIME: Protect Your Videos From Malicious Editingcited by 9
  2. 2023Benchmarking Robustness to Adversarial Image Obfuscationscited by 8
  3. 2024How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Modelcited by 7
  4. 2025CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectorscited by 6
  5. 2023When Vision Fails: Text Attacks Against ViT and OCRcited by 5
  6. 2025Universally Unfiltered and Unseen:Input-Agnostic Multimodal Jailbreaks against Text-to-Image Model Safeguardscited by 5
  7. 2026TRAP: Hijacking VLA CoT-Reasoning via Adversarial Patchescited by 5
  8. 2024Rethinking and Red-Teaming Protective Perturbation in Personalized Diffusion Modelscited by 4
  9. 2026TRAP: Tail-aware Ranking Attack for World-Model Planningcited by 4
  10. 2021Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Trainingcited by 3
  11. 2021Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive Denoisingcited by 3
  12. 2022Causal Scene BERT: Improving object detection by searching for challenging groups of datacited by 3
  13. 2025Adversarial Confusion Attack: Disrupting Multimodal Large Language Modelscited by 3
  14. 2026Just Noticeable Difference Modeling for Deep Visual Featurescited by 3
  15. 2023Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbationscited by 2
  16. 2025Sample-Specific Noise Injection For Diffusion-Based Adversarial Purificationcited by 2
  17. 2022Is Face Recognition Safe from Realizable Attacks?cited by 1
  18. 2024DePatch: Towards Robust Adversarial Patch for Evading Person Detectors in the Real Worldcited by 1
  19. 2021DAFAR: Defending against Adversaries by Feedback-Autoencoder Reconstructioncited by 0
  20. 2024Pre-trained Encoder Inference: Revealing Upstream Encoders In Downstream Machine Learning Servicescited by 0
  21. 2024CapGen:An Environment-Adaptive Generator of Adversarial Patchescited by 0
  22. 2025Probably Approximately Global Robustness Certificationcited by 0
  23. 2026Defining Explainable AI for Requirements Analysiscited by 0
  24. 2026When Surfaces Lie: Exploiting Wrinkle-Induced Attention Shift to Attack Vision-Language Modelscited by 0
  25. 2026AdvScene: Rethinking Adversarial Patch Evaluation Through Scene Robustnesscited by 0
  26. 2026InfraPatch: Cross-Task Targeted Grayscale Patch Attacks on Infrared-Adapted Vision-Language Modelscited by 0
  27. 2026HAND: A Biologically-Inspired Activation Function that Improves Generalisation and Sample Efficiency in Image Classificationcited by 0