research-article

Steganographer Detection via Multi-Scale Embedding Probability Estimation

Authors:

Sheng-Hua Zhong,

Gangshan WuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 15, Issue 4

Article No.: 103, Pages 1 - 23

https://doi.org/10.1145/3352691

Published: 16 December 2019 Publication History

Abstract

Steganographer detection aims to identify the guilty user who utilizes steganographic methods to hide secret information in the spread of multimedia data, especially image data, from a large amount of innocent users on social networks. A true embedding probability map illustrates the probability distribution of embedding secret information in the corresponding images by specific steganographic methods and settings, which has been successfully used as the guidance for content-adaptive steganographic and steganalytic methods. Unfortunately, in real-world situation, the detailed steganographic settings adopted by the guilty user cannot be known in advance. It thus becomes necessary to propose an automatic embedding probability estimation method. In this article, we propose a novel content-adaptive steganographer detection method via embedding probability estimation. The embedding probability estimation is first formulated as a learning-based saliency detection problem and the multi-scale estimated map is then integrated into the CNN to extract steganalytic features. Finally, the guilty user is detected via an efficient Gaussian vote method with the extracted steganalytic features. The experimental results prove that the proposed method is superior to the state-of-the-art methods in both spatial and frequency domains.

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Cited By

Wang JMin WHou SMa SZheng YJiang S(2022)LogoDet-3K: A Large-scale Image Dataset for Logo DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/346678018:1(1-19)Online publication date: 27-Jan-2022
https://dl.acm.org/doi/10.1145/3466780
Yang JDong CZhang FLei MBai X(2021)MSCNN: Steganographer Detection Based on Multi-Scale Convolutional Neural NetworksWireless Algorithms, Systems, and Applications10.1007/978-3-030-85928-2_17(215-226)Online publication date: 25-Jun-2021
https://dl.acm.org/doi/10.1007/978-3-030-85928-2_17
Yang JYang YLei MLin J(2020)ISC : Steganographer Detection Based on Internal Spectral Clustering2020 IEEE International Conference on Progress in Informatics and Computing (PIC)10.1109/PIC50277.2020.9350836(346-351)Online publication date: 18-Dec-2020
https://doi.org/10.1109/PIC50277.2020.9350836

Index Terms

Steganographer Detection via Multi-Scale Embedding Probability Estimation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
2. Security and privacy

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 15, Issue 4

November 2019

322 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3376119

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2019 ACM.

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Publication History

Published: 16 December 2019

Accepted: 01 July 2019

Revised: 01 March 2019

Received: 01 October 2018

Published in TOMM Volume 15, Issue 4

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the Science, Technology and Innovation Commission of Shenzhen Municipality
the Collaborative Innovation Center of Novel Software Technology and Industrialization
the Natural Science Foundation of Guangdong Province
the Shenzhen high-level overseas talents program
the National Science Foundation of China

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Cited By

Wang JMin WHou SMa SZheng YJiang S(2022)LogoDet-3K: A Large-scale Image Dataset for Logo DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/346678018:1(1-19)Online publication date: 27-Jan-2022
https://dl.acm.org/doi/10.1145/3466780
Yang JDong CZhang FLei MBai X(2021)MSCNN: Steganographer Detection Based on Multi-Scale Convolutional Neural NetworksWireless Algorithms, Systems, and Applications10.1007/978-3-030-85928-2_17(215-226)Online publication date: 25-Jun-2021
https://dl.acm.org/doi/10.1007/978-3-030-85928-2_17
Yang JYang YLei MLin J(2020)ISC : Steganographer Detection Based on Internal Spectral Clustering2020 IEEE International Conference on Progress in Informatics and Computing (PIC)10.1109/PIC50277.2020.9350836(346-351)Online publication date: 18-Dec-2020
https://doi.org/10.1109/PIC50277.2020.9350836

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