Faster-RCNN Based Robust Coverless Information Hiding System in Cloud Environment

Zhou, Zhili; Cao, Yi; Wang, Meimin; Fan, Enming; Wu, Q. M. Jonathan

doi:10.1109/access.2019.2955990

Cited by 39 publications

(15 citation statements)

References 19 publications

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“…This method directly expresses the secret message utilizing the characteristics of the carrier [29]. Zhou et al use faster-RCNN for training to find the labels of images to express the secret message [30]. The network architecture based on deep learning has a large transmission overhead and requires a long training time.…”

Section: Muhuri Et Al Implement Image Steganography On Integer Wavele...mentioning

confidence: 99%

Information Hiding Based on Two-Level Mechanism and Look-Up Table Approach

et al. 2022

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Information hiding can be seen everywhere in our daily life, and this technology improves the security of information. The requirements for information security are becoming higher and higher. The coverless information hiding with the help of mapping relationship has high capacity, but there is still a problem in which the secret message cannot find the mapping relationship and the process requires extra storage burden during the transmission. Therefore, on the basis of symmetric reversible watermarking, the paper introduces the two-level mechanism and novel arrangements to solve the problem of sufficient diversity of features and has better capacity and image quality as a whole. Besides, for the security of secret message, this paper designs a new encryption model based on Logistic mapping. This method only employs coverless information hiding of one carrier image to transmit secret message with the help of the two-level mechanism and look-up table. Reversible information hiding is applied to embed the generated location table on the original image so that ensures storage and security. The experiment certifies that the diversity of hash code is increased by using the two-level image mechanism and the quality of the image is excellent, which proves the advantages of the proposed symmetric method over the previous algorithm.

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Section: Muhuri Et Al Implement Image Steganography On Integer Wavele...mentioning

confidence: 99%

Information Hiding Based on Two-Level Mechanism and Look-Up Table Approach

et al. 2022

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“…Generally, the traditional image steganography embeds secret information into a cover image by slightly modifying its content, and thus the modification traces will be inevitably left in the cover image, which makes successful steganalysis possible. The concept of "coverless steganography" was proposed in [11][12][13]. Instead of employing a designated cover image for embedding the secret data, appropriate images that already contain the secret data are chosen as stego-images for secret communication.…”

Section: Image Steganographymentioning

confidence: 99%

Introduction to the special issue on deep learning for real-time information hiding and forensics

Zhou

Yang

Kim

et al. 2020

J Real-Time Image Proc

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“…e generated hidden images and the extracted secret information had good visual effects. Zhou et al [24] proposed an information hiding technology using Faster-RCNN. is solution used Faster-RCNN to identify the contents of the carrier image and expressed secret data through these contents.…”

Section: Introductionmentioning

confidence: 99%

A Novel High-Capacity Information Hiding Scheme Based on Improved U-Net

Liu

Meng

Zheng

et al. 2022

Security and Communication Networks

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With the gradual introduction of deep learning into the field of information hiding, the capacity of information hiding has been greatly improved. Therefore, a solution with a higher capacity and a good visual effect had become the current research goal. A novel high-capacity information hiding scheme based on improved U-Net was proposed in this paper, which combined improved U-Net network and multiscale image analysis to carry out high-capacity information hiding. The proposed improved U-Net structure had a smaller network scale and could be used in both information hiding and information extraction. In the information hiding network, the secret image was decomposed into wavelet components through wavelet transform, and the wavelet components were hidden into image. In the extraction network, the features of the hidden image were extracted into four components, and the extracted secret image was obtained. Both the hiding network and the extraction network of this scheme used the improved U-Net structure, which preserved the details of the carrier image and the secret image to the greatest extent. The simulation experiment had shown that the capacity of this scheme was greatly improved than that of the traditional scheme, and the visual effect was good. And compared with the existing similar solution, the network size has been reduced by nearly 60%, and the processing speed has been increased by 20%. The image effect after hiding the information was improved, and the PSNR between the secret image and the extracted image was improved by 6.3 dB.

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Faster-RCNN Based Robust Coverless Information Hiding System in Cloud Environment

Cited by 39 publications

References 19 publications

Information Hiding Based on Two-Level Mechanism and Look-Up Table Approach

Information Hiding Based on Two-Level Mechanism and Look-Up Table Approach

Introduction to the special issue on deep learning for real-time information hiding and forensics

A Novel High-Capacity Information Hiding Scheme Based on Improved U-Net

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