Distortion Function for Emoji Image Steganography

Shi, Lina; Wang, Zichi; Qian, Zhenxing; Huang, Nannan; Puteaux, Pauline; Zhang, Xinpeng

doi:10.32604/cmc.2019.05768

Cited by 6 publications

(21 citation statements)

References 12 publications

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“…Based on appropriate amplitude weight, we find a suitable value of the motion difference parameter. Subsequently, we compare our proposed method with the basic method using initial distortion functions and Shi et al [37] in Section 4.2 and Section 4.3. We give a subjective evaluation of the stego image in Section 4.2 and a specific analysis of undetectability in Section 4.3.…”

Section: Resultsmentioning

confidence: 99%

“…We use HILL as the initial distortion function. The stegos are generated by the initial function, the function in [37] and ours. The embedding payload is 0.2 bpp.…”

Section: Resultsmentioning

confidence: 99%

“…Saleh and Merzah propose to combine LZW, EzStego, and LSB to embed secret text messages into dynamic GIF images [36]. Shi et al propose to use the STC framework for emoji GIF images by designing a new distortion function [37]. These works have good performances on dynamic GIF steganography.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Steganography Method for Dynamic GIF Images Based on Palette Sort

Lin

Qian

Wang

et al. 2020

Wireless Communications and Mobile Computing

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This paper proposes a new steganography method for hiding data into dynamic GIF (Graphics Interchange Format) images. When using the STC framework, we propose a new algorithm of cost assignment according to the characteristics of dynamic GIF images, including the image palette and the correlation of interframes. We also propose a payload allocation algorithm for different frames. First, we reorder the palette of GIF images to reduce the modifications on pixel values when modifying the index values. As the different modifications on index values would result in different impacts on pixel values, we assign the elements with less impact on pixel values with small embedding costs. Meanwhile, small embedding costs are also assigned for the elements in the regions that the interframe changes are large enough. Finally, we calculate an appropriate payload for each frame using the embedding probability obtained from the proposed distortion function. Experimental results show that the proposed method has a better security performance than state-of-the-art works.

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Section: Resultsmentioning

confidence: 99%

“…We use HILL as the initial distortion function. The stegos are generated by the initial function, the function in [37] and ours. The embedding payload is 0.2 bpp.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A New Steganography Method for Dynamic GIF Images Based on Palette Sort

Lin

Qian

Wang

et al. 2020

Wireless Communications and Mobile Computing

Self Cite

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“…Steganalysis [1] and information hiding are mutually restricted and mutually promoted [2,3]. And there is a more hopeful prospect to carry out the steganalysis work.…”

Section: Introductionmentioning

confidence: 99%

Joint multi-domain feature learning for image steganalysis based on CNN

Wang

Chen

et al. 2020

J Image Video Proc.

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In recent years, researchers have been making great progress in the steganalysis technology based on convolution neural networks (CNN). However, experts ignore the contribution of nonlinear residual and joint domain detection to steganalysis, and how to detect the adaptive steganographic algorithms with low embedding rates is still challenging. In this paper, we propose a CNN steganalysis model that uses a joint domain detection mechanism and a nonlinear detection mechanism. For the nonlinear detection mechanism, based on the spatial rich model (SRM), we introduce the maximum and minimum nonlinear residual feature acquisition method into the model to adapt to the nonlinear distribution of steganography information. For the joint domain detection mechanism, we not only apply the high-pass filters from the SRM for spatial residuals, but also apply the patterns from the discrete cosine transform residual (DCTR) for transformation steganographic impacts, so as to fully capture the interference trace of spatial steganography to transform domain. We also apply a new transfer learning method to improve the model's performance. That is, we apply the low embedding rate steganography samples to initialize the model, because we think that the method makes the network more sensitive than applying high embedding rate steganography samples to initialize the model. The simulation results also confirm this assumption. Combined with the above improved methods, the detection accuracy of the model for WOW and S-UNIWARD is higher than that of SRM+EC, Ye-Net, Xu-Net, Yedroudj-Net and Zhu-Net, which is about 4∼6% higher than that of the optimal Zhu-Net. The results can provide a certain reference for steganalysis and image forensics tasks.

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“…Image steganography is the most popular one in multimedia steganography [8,9]. Traditional image steganography algorithms can be divided into two categories: spatial-domain models and transform-domain ones.…”

Section: Introductionmentioning

confidence: 99%

High-Capacity Robust Image Steganography via Adversarial Network

2020

KSII TIIS

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Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.

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Distortion Function for Emoji Image Steganography

Cited by 6 publications

References 12 publications

A New Steganography Method for Dynamic GIF Images Based on Palette Sort

A New Steganography Method for Dynamic GIF Images Based on Palette Sort

Joint multi-domain feature learning for image steganalysis based on CNN

High-Capacity Robust Image Steganography via Adversarial Network

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