  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 great efforts have been delved
into the transferability across models, surprisingly, less attention has been
paid to the 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. In this paper, 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, which
overcomes existing attacks' limitation of requiring 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, to show how our approach
outperforms existing attacks by degrading performance of multiple CV tasks by a
large margin with only modest perturbations linf=16.
