Generating Robust Adversarial Examples against Online Social Networks
  (OSNs)
Jun Liu Jiantao Zhou Haiwei Wu Weiwei Sun Jinyu Tian
Online Social Networks (OSNs) have blossomed into prevailing transmission
channels for images in the modern era. Adversarial examples (AEs) deliberately
designed to mislead deep neural networks (DNNs) are found to be fragile against
the inevitable lossy operations conducted by OSNs. As a result, the AEs would
lose their attack capabilities after being transmitted over OSNs. In this work,
we aim to design a new framework for generating robust AEs that can survive the
OSN transmission; namely, the AEs before and after the OSN transmission both
possess strong attack capabilities. To this end, we first propose a
differentiable network termed SImulated OSN (SIO) to simulate the various
operations conducted by an OSN. Specifically, the SIO network consists of two
modules: 1) a differentiable JPEG layer for approximating the ubiquitous JPEG
compression and 2) an encoder-decoder subnetwork for mimicking the remaining
operations. Based upon the SIO network, we then formulate an optimization
framework to generate robust AEs by enforcing model outputs with and without
passing through the SIO to be both misled. Extensive experiments conducted over
Facebook, WeChat and QQ demonstrate that our attack methods produce more robust
AEs than existing approaches, especially under small distortion constraints;
the performance gain in terms of Attack Success Rate (ASR) could be more than
60%. Furthermore, we build a public dataset containing more than 10,000 pairs
of AEs processed by Facebook, WeChat or QQ, facilitating future research in the
robust AEs generation. The dataset and code are available at
https://github.com/csjunjun/RobustOSNAttack.git.