Reference

Doan, B.G., Xue, M., Ma, S., Abbasnejad, E. and Ranasinghe, D.C., 2021. TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems. arXiv preprint arXiv:2111.09999.

Link to the research paper: TnTattacks

Bibtex

@article{doan2021tnt,
  title={TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems},
  author={Doan, Bao Gia and Xue, Minhui and Ma, Shiqing and Abbasnejad, Ehsan and Ranasinghe, Damith C},
  journal={arXiv preprint arXiv:2111.09999},
  year={2021}
}

Physical World Deployment Demonstration Videos

Summary

Targeted Attacks on the PubFig Classification Task

• The effectiveness of an example flower TnT
• The robustness of an example flower TnT

Targeted Attacks on ImageNet Large Scale Visual Recognition Task

• The effectiveness of an example flower TnT
• The effectiveness and robustness of miscellaneous examples of flower TnTs
• The effectiveness of a patch trigger
• The robustness of a patch trigger

Demo Videos

Targeted Attack with an input-agnostic TnT, an example flower, from the TnT Generator - PubFig Face Recognition Task

The effectiveness of an example flower TnT

The robustness of an example flower TnT

Targeted Attack with an Input-Agnostic flower Trigger from the TnT Generator - ImageNet classification task

The effectiveness of an example flower TnT

The effectiveness and robustness of miscellaneous examples of flower TnTs

Targeted Attack with an Input-Agnostic Patch from the Adversarial Patch Generator - ImageNet classification task

The effectiveness of a patch trigger

The robustness of a patch trigger