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Steganographer Detection based on Multiclass Dilated Residual Networks

Published: 05 June 2018 Publication History

Abstract

Steganographer detection task is to identify criminal users, who attempt to conceal confidential information by steganography methods, among a large number of innocent users. The significant challenge of the task is how to collect the evidences to identify the guilty user with suspicious images, which are embedded with secret messages generating by unknown steganography and payload. Unfortunately, existing methods for steganalysis were served for the binary classification. It makes them harder to classify the images with different kinds of payloads, especially when the payloads of images in test dataset have not been provided in advance. In this paper, we propose a novel steganographer detection method based on multiclass deep neural networks. In the training stage, the networks are trained to classify the images with six types of payloads. The networks can preserve even strengthen the weak stego signals from secret messages in much larger receptive filed by virtue of residual and dilated residual learning. In the inference stage, the learnt model is used to extract the discriminative features, which can capture the difference between guilty users and innocent users. A series of empirical experimental results demonstrate that the proposed method achieves good performance in spatial and frequency domains even though the embedding payload is low. The proposed method achieves a higher level of robustness of inter-steganographic algorithms and can provide a possible solution to address the payload mismatch problem

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  • (2022)Generative focused feedback residual networks for image steganalysis and hidden information reconstructionApplied Soft Computing10.1016/j.asoc.2022.109550129(109550)Online publication date: Nov-2022
  • (2022)Deep Clustering Network for Steganographer Detection Using Latent Features Extracted from a Novel Convolutional AutoencoderNeural Processing Letters10.1007/s11063-022-10992-655:3(2953-2964)Online publication date: 11-Aug-2022
  • (2022)New Advancements in Cybersecurity: A Comprehensive SurveyBig Data Analytics and Computational Intelligence for Cybersecurity10.1007/978-3-031-05752-6_1(3-17)Online publication date: 2-Sep-2022
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cover image ACM Conferences
ICMR '18: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval
June 2018
550 pages
ISBN:9781450350464
DOI:10.1145/3206025
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Publication History

Published: 05 June 2018

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Author Tags

  1. deep neural networks
  2. multiclass classification
  3. multimedia security
  4. steganographer detection

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  • Research-article

Funding Sources

  • Shenzhen high-level overseas talents program
  • Shenzhen Emerging Industries of the Strategic Basic Research Project
  • Natural Science Foundation of Guangdong Province
  • Tencent Rhinoceros Birds - Scientific Research Foundation for Young Teachers of Shenzhen University (2016)
  • Science and Technology Innovation Commission of Shenzhen
  • National Natural Science Foundation of China

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ICMR '18
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ICMR '18 Paper Acceptance Rate 44 of 136 submissions, 32%;
Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

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  • (2022)Generative focused feedback residual networks for image steganalysis and hidden information reconstructionApplied Soft Computing10.1016/j.asoc.2022.109550129(109550)Online publication date: Nov-2022
  • (2022)Deep Clustering Network for Steganographer Detection Using Latent Features Extracted from a Novel Convolutional AutoencoderNeural Processing Letters10.1007/s11063-022-10992-655:3(2953-2964)Online publication date: 11-Aug-2022
  • (2022)New Advancements in Cybersecurity: A Comprehensive SurveyBig Data Analytics and Computational Intelligence for Cybersecurity10.1007/978-3-031-05752-6_1(3-17)Online publication date: 2-Sep-2022
  • (2021)Steganographer detection via a similarity accumulation graph convolutional networkNeural Networks10.1016/j.neunet.2020.12.026136(97-111)Online publication date: Apr-2021
  • (2021)BMP Color Images Steganographer Detection Based on Deep LearningMobile Multimedia Communications10.1007/978-3-030-89814-4_44(602-612)Online publication date: 2-Nov-2021
  • (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
  • (2020)Computational Intelligence for Information Security: A SurveyIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2019.29234264:5(616-629)Online publication date: Oct-2020
  • (2020)Steganographer Detection Via Enhancement-Aware Graph Convolutional Network2020 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME46284.2020.9102817(1-6)Online publication date: Jul-2020
  • (2019)Steganographer Detection via Multi-Scale Embedding Probability EstimationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/335269115:4(1-23)Online publication date: 16-Dec-2019

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