Image Sanitization in Online Social Networks: A General Framework for Breaking Robust Information Hiding

Abstract

With the development of robust information hiding (RIH) approaches, secret messages can be extracted successfully from stego-data after transmission through lossy channels of online social networks (OSNs). To interrupt illegal covert communications in OSNs, some methods sanitize the uploaded images by image processing operations to destroy the hidden data that may exist. However, none of the existing methods takes the RIH methods that can resist scaling into consideration, while scaling is a common operation in OSNs. In this paper, we first propose a general framework for image sanitization in OSN platforms, which serves as a countermeasure against the RIH. By using such a framework, the secret messages embedded in the upload images can be removed and the quality of the sanitized image can be well maintained. Our framework contains two deep neural networks: Scaling-Net and SC-Net. The Scaling-Net is dedicated to the sanitization of oversized images while the SC-Net is designed for other images. To achieve a good image quality, we also propose a discriminator for adversarial training of the Scaling-Net and SC-Net. Experimental results on different datasets demonstrate that our proposed method outperforms the state-of-the-art methods. The source code and pretrained models are available at our code repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .