Technology of Hiding and Protecting the Secret Image Based on Two-Channel Deep Hiding Network

Chen, Feng; Qing-hua, Xing; Fu-xian, Liu

doi:10.1109/access.2020.2969524

Cited by 14 publications

(16 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the rapid development of deep learning, a large number of academic researchers use deep-learning feature extraction to perform steganalysis and steganalysis of images, audio, video, and text. Chen et al [ 18 ] proposed a two-channel image steganography (TDHN) scheme based on deep learning. First, the carrier image and the secret image are pooled and averaged separately, and then they are

.…”

Section: Related Workmentioning

confidence: 99%

SteganoCNN: Image Steganography with Generalization Ability Based on Convolutional Neural Network

Duan

Liu

Gou

et al. 2020

Entropy

View full text Add to dashboard Cite

Image-to-image steganography is hiding one image in another image. However, hiding two secret images into one carrier image is a challenge today. The application of image steganography based on deep learning in real-life is relatively rare. In this paper, a new Steganography Convolution Neural Network (SteganoCNN) model is proposed, which solves the problem of two images embedded in a carrier image and can effectively reconstruct two secret images. SteganoCNN has two modules, an encoding network, and a decoding network, whereas the decoding network includes two extraction networks. First, the entire network is trained end-to-end, the encoding network automatically embeds the secret image into the carrier image, and the decoding network is used to reconstruct two different secret images. The experimental results show that the proposed steganography scheme has a maximum image payload capacity of 47.92 bits per pixel, and at the same time, it can effectively avoid the detection of steganalysis tools while keeping the stego-image undistorted. Meanwhile, StegaoCNN has good generalization capabilities and can realize the steganography of different data types, such as remote sensing images and aerial images.

show abstract

.…”

Section: Related Workmentioning

confidence: 99%

SteganoCNN: Image Steganography with Generalization Ability Based on Convolutional Neural Network

Duan

Liu

Gou

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…Subsequently, Baluja [ 22 ] displayed the residual map that was obtained by capturing the pixel-wise differences between the hidden image and the cover image and pointed out the research direction of improving steganography’s security; however, the hidden image’s quality has not been essentially improved. In order to further improve the hidden image’s quality and boost the steganographic performance from the source, Chen et al [ 23 ] attempted to deepen and widen the network using various advanced feature fusion strategies and, as a result, a remarkable advancement was achieved.…”

Section: Introductionmentioning

confidence: 99%

“…Although the abovementioned deep learning-based methods [ 20 , 21 , 22 , 23 ] have successfully achieved the goal of concealing image data, the security issues, especially the visual security, have not been well solved. In addition, the recent studies usually design complex models in order to obtain performance improvement, but they ignore the model’s computational complexity in practical application.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Steganography Network for Concealing and Protecting Secret Image Data

Chen

Qing-hua

Sun³

et al. 2022

Entropy

Self Cite

View full text Add to dashboard Cite

The development of Internet technology has provided great convenience for data transmission and sharing, but it also brings serious security problems that are related to data protection. As is detailed in this paper, an enhanced steganography network was designed to protect secret image data that contains private or confidential information; this network consists of a concealing network and a revealing network in order to achieve image embedding and recovery separately. To reduce the system’s computation complexity, we constructed the network’s framework using a down–up structure in order to compress the intermediate feature maps. In order to mitigate the input’s information loss caused by a sequence of convolution blocks, the long skip concatenation method was designed to pass the raw information to the top layer, thus synthesizing high-quality hidden images with fine texture details. In addition, we propose a novel strategy called non-activated feature fusion (NAFF), which is designed to provide stronger supervision for synthetizing higher-quality hidden images and recovered images. In order to further boost the hidden image’s visual quality and enhance its imperceptibility, an attention mechanism-based enhanced module was designed to reconstruct and enhance the salient target, thus covering up and obscuring the embedded secret content. Furthermore, a hybrid loss function that is composed of pixel domain loss and structure domain loss was designed to boost the hidden image’s structural quality and visual security. Our experimental results demonstrate that, due to the elaborate design of the network structure and loss function, our proposed method achieves high levels of imperceptibility and security.

show abstract

“…To increase security, in [11], the attention model and the generative adversarial network are combined to realize the embedding and extraction of secret images. Nowadays, there are relatively few attempts to hide images using the deep neural network itself [12]- [14]. In [13], the convolutional network has an encoder and a decoder.…”

Section: Introductionmentioning

confidence: 99%

High-Capacity Image Steganography Based on Improved FC-DenseNet

et al. 2020

View full text Add to dashboard Cite

Aiming at the problem that the traditional steganography based on carrier modification has the low steganographic capacity, a steganographic scheme based on Fully Convolutional Dense Connection Network (FC-DenseNet) is proposed. Since FC-DenseNet can effectively overcome the problems of gradient dissipation and gradient explosion, and a large number of features are multiplexed, the cascaded secret image and carrier image can reconstruct good image quality after entering the network. Effectively improve steganographic capacity. First, we reset the number of input channels of the first convolution block of FC-DenseNet and the number of output channels of the last convolution block and deleted the LogSoftmax() function. On the sender side, after the concatenated secret image and carrier image pass through the hidden network FC-DenseNet, the secret image is embedded in the carrier image to obtain a stego-image. At the receiving side, the extraction network reconstructs the secret image from the stegoimage. Experimental results show that our proposed steganography scheme not only has a high Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity(SSIM) but also can realize large-capacity image steganography, with an average image payload capacity of 23.96 bit per pixel.

show abstract

Technology of Hiding and Protecting the Secret Image Based on Two-Channel Deep Hiding Network

Cited by 14 publications

References 39 publications

SteganoCNN: Image Steganography with Generalization Ability Based on Convolutional Neural Network

SteganoCNN: Image Steganography with Generalization Ability Based on Convolutional Neural Network

An Enhanced Steganography Network for Concealing and Protecting Secret Image Data

High-Capacity Image Steganography Based on Improved FC-DenseNet

Contact Info

Product

Resources

About