# Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study

## TL;DR

This paper investigates the security vulnerabilities of cloud-based image detectors to black-box adversarial attacks, demonstrating high success rates and proposing potential defenses.

## Contribution

It introduces four semantics-aware attack methods using only black-box API interactions and provides the first extensive empirical study on their effectiveness against real-world cloud detectors.

## Key findings

- Attack success rate reaches approximately 100%
- Semantic segmentation based attacks succeed over 90%
- Effective defenses are proposed for real-world applications

## Abstract

Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification, object identification, illegal image detection, etc. While recent works extensively demonstrated that deep learning classification models are vulnerable to adversarial examples, cloud-based image detection models, which are more complicated than classifiers, may also have similar security concern but not get enough attention yet. In this paper, we mainly focus on the security issues of real-world cloud-based image detectors. Specifically, (1) based on effective semantic segmentation, we propose four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs; and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based image detectors. Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100%, and the semantic segmentation based attacks have a success rate over 90% among different detection services, such as violence, politician, and pornography detection. We also proposed several possible defense strategies for these security challenges in the real-life situation.