Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction

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...

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Bibliographic Details
Main Authors: Lu, Yantao, Jia, Yunhan, Wang, Jianyu, Li, Bai, Chai, Weiheng, Carin, Lawrence, Velipasalar, Senem
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Computer vision
Dispersion
Feature extraction
Image segmentation
Machine learning
Task analysis
Online Access:Request full text
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Summary: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.
ISSN:2575-7075
DOI:10.1109/CVPR42600.2020.00102