Steganographic visual story with mutual-perceived joint attention

Author:

Guo Yanyang,Wu HanzhouORCID,Zhang Xinpeng

Abstract

AbstractSocial media plays an increasingly important role in providing information and social support to users. Due to the easy dissemination of content, as well as difficulty to track on the social network, we are motivated to study the way of concealing sensitive messages in this channel with high confidentiality. In this paper, we design a steganographic visual stories generation model that enables users to automatically post stego status on social media without any direct user intervention and use the mutual-perceived joint attention (MPJA) to maintain the imperceptibility of stego text. We demonstrate our approach on the visual storytelling (VIST) dataset and show that it yields high-quality steganographic texts. Since the proposed work realizes steganography by auto-generating visual story using deep learning, it enables us to move steganography to the real-world online social networks with intelligent steganographic bots.

Funder

National Natural Science Foundation of China

Shanghai "Chen Guang" Project

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Information Systems,Signal Processing

Reference31 articles.

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