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  3. Dediattack: Enhancing Transferability Of Unrestricted Adversarial Examples Via Deformation-constrained Diffusion.
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  3. Dediattack: Enhancing Transferability Of Unrestricted Adversarial Examples Via Deformation-constrained Diffusion.

Related Experiment Videos

DeDiAttack: Enhancing Transferability of Unrestricted Adversarial Examples via Deformation-Constrained Diffusion.

Bin Qu1, Anjie Peng1,2, Shijie Zhao1,2

  • 1Jianghuai Advanced Technology Center, Hefei 230037, China.

Sensors (Basel, Switzerland)
|May 13, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

DeDiAttack generates natural unrestricted adversarial examples (UAEs) by constraining diffusion model deformations. This method enhances black-box transferability, improving adversarial attack robustness for vision systems.

Keywords:
black-box transferabilitydeformation-constraineddiffusion modelsunrestricted adversarial attacksvisual naturalness

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep neural networks (DNNs) are vulnerable to adversarial examples (AEs).
  • Traditional AEs often have unnatural artifacts, while existing unrestricted adversarial examples (UAEs) struggle with visual fidelity and black-box transferability.
  • Diffusion-based attacks exhibit poor transferability to unknown black-box models, and color-based attacks introduce noticeable visual mutations.

Purpose of the Study:

  • To develop a novel unrestricted adversarial attack method that balances visual fidelity and black-box transferability.
  • To address the limitations of existing UAE generation techniques, particularly concerning distribution shifts and surrogate model overfitting in diffusion models.
  • To enhance the robustness evaluation of vision-based sensing and imaging systems.

Main Methods:

  • Proposed DeDiAttack, an unrestricted attack based on deformation-constrained diffusion.
  • Utilized manifold prior knowledge of diffusion models to translate elastic deformations into smooth fluid changes.
  • Incorporated elastic deformation during denoising to guide surrogate models toward highly transferable features, preventing distribution shifts and overfitting.

Main Results:

  • DeDiAttack effectively eliminates unnatural artifacts, generating highly natural and transferable UAEs.
  • Demonstrated superior performance over existing attacks in extensive black-box experiments.
  • Achieved a 7.2% improvement in black-box transferability on the ViT-B surrogate model.

Conclusions:

  • DeDiAttack successfully generates natural and transferable UAEs, overcoming limitations of prior methods.
  • The proposed method offers a significant advancement in adversarial attack research for DNNs.
  • DeDiAttack serves as a valuable tool for assessing the robustness of vision-based systems.