Steganographer Detection Via Enhancement-Aware Graph Convolutional Network

Zhi Zhang, Mingjie Zheng, Sheng Hua Zhong, Yan Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Steganographer detection aims to find guilty users who hide secret information in images or other multimedia data in the social network. In existing work, the distances between users are calculated based on the distributions of all images shared by the corresponding users, then users lying an abnormal distance from others are detected as guilty users. This flattened method is difficult to grasp the nuances of the guilty and innocent users. In this paper, we are the first to propose a graph-based deep learning framework for steganographer detection. The proposed Enhancement-aware Graph Convolutional Network (EGCN) represents each user as a weighted complete graph and learns to highlight the differences between guilty users and innocent users based on the structured graph. Compared with the state-of-the-art method and other representative graph-based models, the proposed framework demonstrates its effectiveness across image domains, and even under the context of large-scale social media scenario.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Multimedia and Expo, ICME 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728113319
Publication statusPublished - Jul 2020
Event2020 IEEE International Conference on Multimedia and Expo, ICME 2020 - London, United Kingdom
Duration: 6 Jul 202010 Jul 2020

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


Conference2020 IEEE International Conference on Multimedia and Expo, ICME 2020
Country/TerritoryUnited Kingdom


  • Graph convolutional network
  • Graph-based classification
  • Image steganographer detection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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