Skip to main content

Enhancing cross-task black-box transferability of adversarial examples with dispersion reduction

Publication ,  Conference
Lu, Y; Jia, Y; Wang, J; Li, B; Chai, W; Carin, L; Velipasalar, S
Published in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
January 1, 2020

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they remain adversarial even against other models. Although significant effort has been devoted to the transferability across models, surprisingly little attention has been paid to cross-task transferability, which represents the real-world cybercriminal's situation, where an ensemble of different defense/detection mechanisms need to be evaded all at once. We investigate the transferability of adversarial examples across a wide range of real-world computer vision tasks, including image classification, object detection, semantic segmentation, explicit content detection, and text detection. Our proposed attack minimizes the “dispersion” of the internal feature map, overcoming the limitations of existing attacks, that require task-specific loss functions and/or probing a target model. We conduct evaluation on open-source detection and segmentation models, as well as four different computer vision tasks provided by Google Cloud Vision (GCV) APIs. We demonstrate that our approach outperforms existing attacks by degrading performance of multiple CV tasks by a large margin with only modest perturbations.

Duke Scholars

Published In

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

DOI

ISSN

1063-6919

Publication Date

January 1, 2020

Start / End Page

937 / 946
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Lu, Y., Jia, Y., Wang, J., Li, B., Chai, W., Carin, L., & Velipasalar, S. (2020). Enhancing cross-task black-box transferability of adversarial examples with dispersion reduction. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 937–946). https://doi.org/10.1109/CVPR42600.2020.00102
Lu, Y., Y. Jia, J. Wang, B. Li, W. Chai, L. Carin, and S. Velipasalar. “Enhancing cross-task black-box transferability of adversarial examples with dispersion reduction.” In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 937–46, 2020. https://doi.org/10.1109/CVPR42600.2020.00102.
Lu Y, Jia Y, Wang J, Li B, Chai W, Carin L, et al. Enhancing cross-task black-box transferability of adversarial examples with dispersion reduction. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2020. p. 937–46.
Lu, Y., et al. “Enhancing cross-task black-box transferability of adversarial examples with dispersion reduction.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, pp. 937–46. Scopus, doi:10.1109/CVPR42600.2020.00102.
Lu Y, Jia Y, Wang J, Li B, Chai W, Carin L, Velipasalar S. Enhancing cross-task black-box transferability of adversarial examples with dispersion reduction. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2020. p. 937–946.

Published In

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

DOI

ISSN

1063-6919

Publication Date

January 1, 2020

Start / End Page

937 / 946