mlsanityData to 13 Oct 2026

Unrestricted Adversarial Attacks on ImageNet Competition

arXiv 2110.09903 ↗First posted 17 Oct 2021cs.CVcited by 23

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Abstract (arXiv)

Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-world, the attacker does not need to comply with this restriction. In fact, more threats to the deep model come from unrestricted adversarial examples, that is, the attacker makes large and visible modifications on the image, which causes the model classifying mistakenly, but does not affect the normal observation in human perspective. Unrestricted adversarial attack is a popular and practical direction but has not been studied thoroughly. We organize this competition with the purpose of exploring more effective unrestricted adversarial attack algorithm, so as to accelerate the academical research on the model robustness under stronger unbounded attacks. The competition is held on the TianChi platform (urlhttps://tianchi.aliyun.com/competition/entrance/531853/introduction) as one of the series of AI Security Challengers Program.

Key facts. Cited by 23 papers in the map (data through 13 Oct 2026). Builds on 32 of its 36 references that are in the map. First posted on arXiv 17 Oct 2021.

Builds on

32 of 36 references.

References · 32

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,082
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,476
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,336
  4. 2016Densely Connected Convolutional Networkscited by 6,707
  5. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,379
  6. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,457
  7. 2013Intriguing properties of neural networkscited by 5,137
  8. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,992
  9. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,862
  10. 2016Identity Mappings in Deep Residual Networkscited by 1,962
  11. 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,847
  12. 2016Adversarial examples in the physical worldcited by 1,767
20 more
  1. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  2. 2019Natural Adversarial Examplescited by 1,405
  3. 2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,101
  4. 2019Learning Robust Global Representations by Penalizing Local Predictive Powercited by 1,012
  5. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 597
  6. 2016Delving into Transferable Adversarial Examples and Black-box Attackscited by 536
  7. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
  8. 2019Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attackscited by 441
  9. 2019GhostNet: More Features from Cheap Operationscited by 410
  10. 2020Perceptual Adversarial Robustness: Defense Against Unseen Threat Modelscited by 137
  11. 2020Adversarial Camouflage: Hiding Physical-World Attacks with Natural Stylescited by 126
  12. 2019Improving Black-box Adversarial Attacks with a Transfer-based Priorcited by 121
  13. 2019ColorFool: Semantic Adversarial Colorizationcited by 89
  14. 2019Unrestricted Adversarial Examples via Semantic Manipulationcited by 84
  15. 2017APE-GAN: Adversarial Perturbation Elimination with GANcited by 57
  16. 2018Understanding and Enhancing the Transferability of Adversarial Examplescited by 41
  17. 2019AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examplescited by 17
  18. 2019Should Adversarial Attacks Use Pixel p-Norm?cited by 10
  19. 2019EdgeFool: An Adversarial Image Enhancement Filtercited by 10
  20. 2021Improving Adversarial Transferability with Gradient Refiningcited by 4

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Citing papers per year: 2022: 3; 2023: 7; 2024: 8; 2025: 5.

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  1. 2022Physics-Informed Machine Learning: A Survey on Problems, Methods and Applicationscited by 90
  2. 2024MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Modelscited by 53
  3. 2023AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Modelscited by 46
  4. 2023Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Drivingcited by 37
  5. 2022Towards Transferable Unrestricted Adversarial Examples with Minimum Changescited by 13
  6. 2025Towards Safe Reasoning in Large Reasoning Models via Corrective Interventioncited by 11
  7. 2023Evading Forensic Classifiers with Attribute-Conditioned Adversarial Facescited by 6
  8. 2022Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generatorcited by 5
  9. 2025Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigationcited by 4
  10. 2023Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identificationcited by 2
  11. 2024SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledgecited by 1
  12. 2025SCOOTER: A Human Evaluation Framework for Unrestricted Adversarial Examplescited by 1
11 more
  1. 2023Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adaptercited by 0
  2. 2023GMTR: Graph Matching Transformerscited by 0
  3. 2023DarkShot: Lighting Dark Images with Low-Compute and High-Qualitycited by 0
  4. 2024TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processingcited by 0
  5. 2024TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networkscited by 0
  6. 2024T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Imagecited by 0
  7. 2024Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithmscited by 0
  8. 2024Towards General Deepfake Detection with Dynamic Curriculumcited by 0
  9. 2024Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphscited by 0
  10. 2025Mask6D: Masked Pose Priors For 6D Object Pose Estimationcited by 0
  11. 2025Keep It Real: Challenges in Attacking Compression-Based Adversarial Purificationcited by 0