TY - GEN
T1 - Steganographer Detection Via Enhancement-Aware Graph Convolutional Network
AU - Zhang, Zhi
AU - Zheng, Mingjie
AU - Zhong, Sheng Hua
AU - Liu, Yan
N1 - Funding Information:
This work was supported by the Natural Science Foundation of Guangdong Province (No. 2019A1515011181), the Science and Technology Innovation Commission of Shenzhen under Grant (No. JCYJ20190808162613130), the Shenzhen high-level overseas talents program, the National Engineering Laboratory for Big Data System Computing Technology. ∗S.-h. Zhong is the corresponding author of this paper. †Z. Zhang and M. Zheng contributed equally to this paper.
Funding Information:
This work was supported by the Natural Science Foundation of Guangdong Province (No. 2019A1515011181), the Science and Technology Innovation Commission of Shenzhen under Grant (No. JCYJ20190808162613130), the Shenzhen high-level overseas talents program, the National Engineering Laboratory for Big Data System Computing Technology.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - Graph convolutional network
KW - Graph-based classification
KW - Image steganographer detection
UR - http://www.scopus.com/inward/record.url?scp=85090383846&partnerID=8YFLogxK
U2 - 10.1109/ICME46284.2020.9102817
DO - 10.1109/ICME46284.2020.9102817
M3 - Conference article published in proceeding or book
AN - SCOPUS:85090383846
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2020 IEEE International Conference on Multimedia and Expo, ICME 2020
PB - IEEE Computer Society
T2 - 2020 IEEE International Conference on Multimedia and Expo, ICME 2020
Y2 - 6 July 2020 through 10 July 2020
ER -