Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction
This addresses a cybersecurity threat for real-world deep learning deployments where multiple detection systems must be evaded simultaneously, representing an incremental improvement in attack techniques.
The paper tackles the problem of adversarial example transferability across multiple real-world computer vision tasks, proposing a method that reduces feature map dispersion to outperform existing attacks, degrading performance by a large margin with modest perturbations.
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they maintain their effectiveness even against other models. With great efforts delved into the transferability of adversarial examples, surprisingly, less attention has been paid to its impact on real-world deep learning deployment. In this paper, we investigate the transferability of adversarial examples across a wide range of real-world computer vision tasks, including image classification, explicit content detection, optical character recognition (OCR), and object detection. It represents the cybercriminal's situation where an ensemble of different detection mechanisms need to be evaded all at once. We propose practical attack that overcomes existing attacks' limitation of requiring task-specific loss functions by targeting on the `dispersion' of internal feature map. We report evaluation on four different computer vision tasks provided by Google Cloud Vision APIs to show how our approach outperforms existing attacks by degrading performance of multiple CV tasks by a large margin with only modest perturbations.