mlsanityData to 14 Oct 2026

Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping

arXiv 2402.03951 ↗First posted 6 Feb 2024cs.CV cs.AIcited by 18

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Abstract (arXiv)

Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems. To address this problem, many transferability enhancement approaches (e.g., input transformation and model augmentation) have been proposed. However, they show poor performances in attacking systems having different model genera from the surrogate model. In this paper, we propose a novel and generic attacking strategy, called Deformation-Constrained Warping Attack (DeCoWA), that can be effectively applied to cross model genus attack. Specifically, DeCoWA firstly augments input examples via an elastic deformation, namely Deformation-Constrained Warping (DeCoW), to obtain rich local details of the augmented input. To avoid severe distortion of global semantics led by random deformation, DeCoW further constrains the strength and direction of the warping transformation by a novel adaptive control strategy. Extensive experiments demonstrate that the transferable examples crafted by our DeCoWA on CNN surrogates can significantly hinder the performance of Transformers (and vice versa) on various tasks, including image classification, video action recognition, and audio recognition. Code is made available at https://github.com/LinQinLiang/DeCoWA.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 40 of its 55 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

40 of 55 references.

References · 40

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28 more
  1. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  2. 2017The Kinetics Human Action Video Datasetcited by 1,909
  3. 2018SlowFast Networks for Video Recognitioncited by 1,867
  4. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,432
  5. 2016Adversarial Machine Learning at Scalecited by 936
  6. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 599
  7. 2019PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognitioncited by 574
  8. 2021Intriguing Properties of Vision Transformerscited by 446
  9. 2019Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attackscited by 442
  10. 2019Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attackscited by 355
  11. 2019High Frequency Component Helps Explain the Generalization of Convolutional Neural Networkscited by 310
  12. 2021Understanding Robustness of Transformers for Image Classificationcited by 249
  13. 2021Enhancing the Transferability of Adversarial Attacks through Variance Tuningcited by 244
  14. 2018Spatially Transformed Adversarial Examplescited by 195
  15. 2021On the Robustness of Vision Transformers to Adversarial Examplescited by 167
  16. 2019Enhancing Adversarial Example Transferability with an Intermediate Level Attackcited by 140
  17. 2022Frequency Domain Model Augmentation for Adversarial Attackcited by 130
  18. 2021Feature Importance-aware Transferable Adversarial Attackscited by 127
  19. 2021On the Adversarial Robustness of Vision Transformerscited by 106
  20. 2022A Comprehensive Survey of Image Augmentation Techniques for Deep Learningcited by 97
  21. 2021Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferabilitycited by 84
  22. 2021Towards Transferable Adversarial Attacks on Vision Transformerscited by 80
  23. 2022Frequency-driven Imperceptible Adversarial Attack on Semantic Similaritycited by 77
  24. 2019FDA: Feature Disruptive Attackcited by 56
  25. 2019Heuristic Black-box Adversarial Attacks on Video Recognition Modelscited by 37
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 6; 2026: 5.

18 papers.

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  1. 2024Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Modelscited by 36
  2. 2024Transferable Adversarial Attacks on SAM and Its Downstream Modelscited by 21
  3. 2024Boosting Adversarial Transferability with Low-Cost Optimization via Maximin Expected Flatnesscited by 7
  4. 2025Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuningcited by 5
  5. 2025GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithmcited by 3
  6. 2024Cloud Adversarial Example Generation for Remote Sensing Image Classificationcited by 2
  7. 2024GADT: Enhancing Transferable Adversarial Attacks through Gradient-guided Adversarial Data Transformationcited by 2
  8. 2024S4ST: A Strong, Self-transferable, faSt, and Simple Scale Transformation for Transferable Targeted Attackcited by 1
  9. 2025Improving the Transferability of Adversarial Attacks by an Input Transposecited by 1
  10. 2025ViT-EnsembleAttack: Augmenting Ensemble Models for Stronger Adversarial Transferability in Vision Transformerscited by 1
  11. 2026OTI: A Model-free and Visually Interpretable Measure of Image Attackabilitycited by 1
  12. 2024Enhancing Transferability of Targeted Adversarial Examples: A Self-Universal Perspectivecited by 0
6 more
  1. 2025Dynamic Parameter Optimization for Highly Transferable Transformation-Based Attackscited by 0
  2. 2025Enhancing Adversarial Transferability through Block Stretch and Shrinkcited by 0
  3. 2026SRAW-Attack: Space-Reweighted Adversarial Warping Attack for SAR Target Recognitioncited by 0
  4. 2026Devling into Adversarial Transferability on Image Classification: Review, Benchmark, and Evaluationcited by 0
  5. 2026A Unified Spatial Alignment Framework for Highly Transferable Transformation-Based Attacks on Spatially Structured Taskscited by 0
  6. 2026Perspective-Invariant Attack with Enhanced Transferability of Adversarial Examplescited by 0