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Feature Aggregation Networks for Image Steganalysis

Published: 23 June 2020 Publication History

Abstract

Since convolutional neural networks have shown remarkable performance on various computer vision tasks, many network architectures for image steganalysis have been introduced. Many of them use fixed preprocessing filters for stable learning, which have a disadvantage of limited use of the information of the input image. The recently introduced end-to-end learning method uses a structure that limits the number of channels of feature maps close to the input and stacks residual blocks. This method has limitations in generating feature maps of various levels and resolutions that can be effective for steganalysis. We therefore propose the feature aggregation-based steganalysis networks: expand the number of channels of convolutional blocks close to the input data, aggregate feature maps of various levels and resolutions, and utilize rich information to improve steganalysis performance. In addition, the capped activation function is applied to obtain better generalization performance. The proposed method outperforms the state-of-the-art steganalysis on detection of the advanced steganography algorithms J-UNIWARD and UED, for JPEG quality factor 75 and 95.

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Cited By

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  • (2021)Steganalysis of Digital Images Using Deep Fractal NetworkIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.30525208:3(599-606)Online publication date: Jun-2021

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cover image ACM Conferences
IH&MMSec '20: Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security
June 2020
177 pages
ISBN:9781450370509
DOI:10.1145/3369412
© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 23 June 2020

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Author Tags

  1. JPEG
  2. convolutional neural networks
  3. feature aggregation
  4. steganalysis
  5. steganography

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Overall Acceptance Rate 128 of 318 submissions, 40%

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  • (2021)Steganalysis of Digital Images Using Deep Fractal NetworkIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.30525208:3(599-606)Online publication date: Jun-2021

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