HidingGAN: High Capacity Information Hiding with Generative Adversarial Network

Wang, Zihan; Gao, Neng; Wang, Xin; Xiang, Ji; Zha, Daren; Li, Linghui

doi:10.1111/cgf.13846

Cited by 19 publications

(13 citation statements)

References 19 publications

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“…Steganography on various representations. Recent stenography methods [36], [37], [38], [39], [40] conceal confidential information in images, videos, or audios into a reversible container, from which the secret information is recoverable. Among them, the most widely-adopted media is 2D digital image.…”

Section: Point Cloud Downsamplingmentioning

confidence: 99%

Point Set Self-Embedding

Wong

2023

IEEE Trans. Visual. Comput. Graphics

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This work presents an innovative method for point set self-embedding, that encodes the structural information of a dense point set into its sparser version in a visual but imperceptible form. The self-embedded point set can function as the ordinary downsampled one and be visualized efficiently on mobile devices. Particularly, we can leverage the self-embedded information to fully restore the original point set for detailed analysis on remote servers. This task is challenging, since both the self-embedded point set and the restored point set should resemble the original one. To achieve a learnable self-embedding scheme, we design a novel framework with two jointly-trained networks: one to encode the input point set into its self-embedded sparse point set and the other to leverage the embedded information for inverting the original point set back. Further, we develop a pair of up-shuffle and down-shuffle units in the two networks, and formulate loss terms to encourage the shape similarity and point distribution in the results. Extensive qualitative and quantitative results demonstrate the effectiveness of our method on both synthetic and real-scanned datasets. The source code and trained models will be publicly available at https://github.com/liruihui/Self-Embedding.

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Section: Point Cloud Downsamplingmentioning

confidence: 99%

Point Set Self-Embedding

Wong

2023

IEEE Trans. Visual. Comput. Graphics

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“…In order to improve the persuasiveness of the proposed method, we compare HCISNet with the state-of-the-are methods and similar works, such as HiDDeN [20], SteganoGAN [23], CSIS [44], SteGAN [45] and HidingGAN [51]. We reproduce the HiDDeN and SteganoGAN, where the training batch sizes are set to six.…”

Section: Settingsmentioning

confidence: 99%

“…Besides, the other parameters are same as papers. However, since there are uncertain parameters in CSIS, SteGAN and HidingGAN, we refer to the results of papers [44,45,51].…”

Section: Settingsmentioning

confidence: 99%

HCISNet: Higher‐capacity invisible image steganographic network

Liu

et al. 2021

IET Image Processing

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Information hiding as a crucial method for multimedia security and privacy protection, has received a substantial amount of attention. However, the most existing methods focus on the resistance of steganalysis and the robustness of information, resulting in low capacity. In this paper, to further increase the capacity of information hiding, a high-capacity adversarial image steganography model with end-to-end manner, termed as HCISNet is proposed. First, an enhanced Dense Atrous Spatial Pyramid Pooling module is presented to learn more semantic information in cover image, which can adaptively embed more message in redundant regions with rich textures. Then, a modified discriminator network with lightweight residual block is employed to assist the encoder network with optimal highcapacity solution. Meanwhile, the multiple objective function with the perceptual loss and several training tricks are developed, which can improve the visual and perceptual consistency of the original and the steganographic image, further enhancing the capacity. Finally, experiments and analysis are conducted on three public datasets, where this model has better imperceptibility, higher security and greater capacity. Under same experimental conditions, the cover image can embed 5.68 bits per pixel (BPP), far exceeding the previous highest value of 4.4 BPP in state-of-the-art methods in the authors' knowledge.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…GAN network performed well in the field of image generation, so it also had good visual effects when generating images containing secret information. Yang et al [22] and Wang et al [23] also used the GAN network idea to generate hidden images in the confrontation between the generator and the discriminator.…”

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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HidingGAN: High Capacity Information Hiding with Generative Adversarial Network

Cited by 19 publications

References 19 publications

Point Set Self-Embedding

Point Set Self-Embedding

HCISNet: Higher‐capacity invisible image steganographic network

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

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