Defeating data hiding in social networks using generative adversarial network

EURASIP Journal on Image and Video Processing

Guorui Feng¹  Huaqi Wang¹  Xinpeng Zhang²  Zhenxing Qian² 

[1] School of Communication and Information Engineering, Shanghai University;Shanghai Institute of Intelligent Electronics and Systems, School of Computer Science, Fudan University;

关键词: Information hiding;    Social networks;    Steganography;    Steganalysis;   

DOI  :  10.1186/s13640-020-00518-2

来源: DOAJ

【 摘 要 】

Abstract As a large number of images are transmitted through social networks every moment, terrorists may hide data into images to convey secret data. Various types of images are mixed up in the social networks, and it is difficult for the servers of social networks to detect whether the images are clean. To prevent the illegal communication, this paper proposes a method of defeating data hiding by removing the secret data without impacting the original media content. The method separates the clean images from illegal images using the generative adversarial network (GAN), in which a deep residual network is used as a generator. Therefore, hidden data can be removed and the quality of the processed images can be well maintained. Experimental results show that the proposed method can prevent secret transmission effectively and preserve the processed images with high quality.

【 授权许可】

Unknown