Artificial neural network for steganography

Husien, Sabah; Badi, Haitham

doi:10.1007/s00521-014-1702-1

Cited by 33 publications

(13 citation statements)

References 12 publications

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“…Nguyen's proposed scheme is superior to previous scheme in terms of tamper detection and image quality. In view of the recent excellent results obtained by combining deep neural networks with steganalysis [18]- [21], there are relatively few attempts to incorporate neural networks into the hidden process itself [22]- [25]. Some of these researchers used deep neural networks (DNN) to use the binary representation of text messages in the image to select which LSBs were replaced.…”

Section: Introductionmentioning

confidence: 99%

Reversible Image Steganography Scheme Based on a U-Net Structure

et al. 2019

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Traditional steganography methods often hide secret data by establishing a mapping relationship between secret data and a cover image or directly in a noisy area, but has a low embedding capacity. Based on the thought of deep learning, in this paper, we propose a new image steganography scheme based on a U-Net structure. First, in the form of paired training, the trained deep neural network includes a hiding network and an extraction network; then, the sender uses the hiding network to embed the secret image into another full-size image without any modification and sends it to the receiver. Finally, the receiver uses the extraction network to reconstruct the secret image and original cover image correctly. The experimental results show that the proposed scheme compresses and distributes the information of the embedded secret image into all available bits in the cover image, which not only solves the obvious visual cues problem, but also increases the embedding capacity.

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Section: Introductionmentioning

confidence: 99%

Reversible Image Steganography Scheme Based on a U-Net Structure

et al. 2019

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“…Husein & Badi [33] 2014 Used two phased simulation package as Artificial neural network where 1 st phase asks for embedding and 2 nd phase to read the concealed message and used LevenbergMarquardt (LM) also for training the N.N Afrakhteh et. al.…”

Section: Literature Surveymentioning

confidence: 99%

Steganography techniques: a survey

2017

IJLTET

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“…Frequency-domain ANN is used [ 26 ] to augment the embedding capacity. Spatial domain based ANN is utilized [ 27 ] to realize good approximation capacity, faster convergence, and a more stable performance surface. This type of ANN is also used [ 28 ] to increase the approximation capacity and minimize distortion.…”

Section: Introductionmentioning

confidence: 99%

Extreme learning machine based optimal embedding location finder for image steganography

et al. 2017

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In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.

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Artificial neural network for steganography

Cited by 33 publications

References 12 publications

Reversible Image Steganography Scheme Based on a U-Net Structure

Reversible Image Steganography Scheme Based on a U-Net Structure

Steganography techniques: a survey

Extreme learning machine based optimal embedding location finder for image steganography

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