High-Capacity Image Steganography Algorithm Based on Image Style Transfer

Bi, Xinliang; Yang, Xiaoyuan; Wang, Chao; Liu, Jia

doi:10.1155/2021/4179340

Cited by 12 publications

(5 citation statements)

References 34 publications

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“…The output of the first level is sent to the second level, where it is concealed under the cover image. The suggested system [11] creates a conversion link between the sensitive information and the stego image by using an ISTNet steganography algorithm to create a stego image that is relatively similar to the original cover image.…”

Section: Related Workmentioning

confidence: 99%

Designing a Model for Hiding Images in RGB Cover Image Based Scrambling and Encryption Methods

Hadi,

Hussein,

Al Baity

2023

IJSSE

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In the digital world, one of the crucial issues is protecting information transmitted over a public network; therefore, encryption and steganography methods must be used to raise the level of data security. This paper invests scrambling and encryption techniques to protect data and compress it to reduce its size, thus increasing system performance. The system is built on protecting gray images after passing a set of steps. The first step denotes the scrambling stage that scatters the locations of gray images by adopting a logistic map method to make it difficult for intruders. The second step contains scattering the image again using the same method but with a different equation and then performing encryption based on the xor operation. The third step represents embedding the data and includes dividing the RGB cover image into three bands where each band is divided into (4×4) blocks, and the bits are stored in the location (2,2) from each band. It tested the system's efficiency by conducting experiments on a set of grayscale images and then using PSNR as a measurement function, where the result was 67.9705.

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Section: Related Workmentioning

confidence: 99%

Designing a Model for Hiding Images in RGB Cover Image Based Scrambling and Encryption Methods

Hadi,

Hussein,

Al Baity

2023

IJSSE

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“…In 2021, Li et al [23] proposed an image steganography scheme based on style migration and quaternion exponential moments, combining the geometric invariance of quaternion exponential moments to achieve the embedding of 32 × 32 binary images, then perform style migration for transmission, and finally go through a de-styled network and then extract the secret image with high robustness and security, as shown in figure 6. Subsequently, Bi et al [24] proposed an ISTNet scheme to hide a grayscale image into a stylized image during style migration, and the generated secret-containing image is indistinguishable from the artistic image on the Internet, as shown in figure 7. An improved image steganography framework based on Y-channel neural style migration was proposed by Lin et al [25], integrating Y-channel embedding initially and style change of the image after that.…”

Section: Information Hiding Model Based On Neural Style Transfermentioning

confidence: 99%

Hiding Image within Image Based on Deep Learning

Yang¹,

Zhang²,

Wu³

et al. 2022

J. Phys.: Conf. Ser.

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Information hiding technology is a technology that transmits secret information through a carrier, and is a research hotspot in the field of information security. The traditional information hiding algorithm relies on the meticulous design of human beings, and obtains the dense image through modification. With the development of deep learning, the integration of information hiding technology and deep learning has resulted in many information hiding technologies based on deep learning. Among them, image hiding has become a research hotspot due to its large steganographic capacity. Therefore, this paper reviews the information hiding technology based on deep learning. According to the difference of hidden models, it is analysed from four aspects: (1) information hiding model based on encoder-decoder; (2) information hiding model based on generative adversarial network; (3) information hiding model based on invertible network; (4) information hiding model based on neural network information hiding models for style transfer. Finally, these models are analysed and compared, and the future development direction is discussed and prospected.

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“…Attention mechanism [20][21][22], as an efficient low-cost way to enhance features, has been widely used in various visual tasks [23][24][25][26]. (e early attention mechanism in the visual field is the squeeze-andexcitation module (SE) proposed by SENet [20].…”

Section: Attention Mechanismmentioning

confidence: 99%

Image Forgery Detection Using Tamper-Guided Dual Self-Attention Network with Multiresolution Hybrid Feature

Pei

Wei

et al. 2022

Security and Communication Networks

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Image forgery detection can efficiently capture the difference between the tampered area and the nontampered area. However, existing work usually overemphasizes pixel-level localization, ignoring image-level detection. As a result, false detection for tampered image maybe cause a large number of false positives. To address this problem, we propose an end-to-end fully convolutional neural network. In this framework, multiresolution hybrid features from RGB stream and noise stream are firstly fused to learn visual artifacts and compression inconsistency artifacts, which can efficiently identify the tampered images. Furthermore, a tamper-guided dual self-attention (TDSA) module is designed, which can focus the network’s attention on the tampered areas and segment them from the image by capturing the difference between the tampered area and the nontampered area. Extensive experiments demonstrate that compared to existing schemes, our scheme can simultaneously effectively achieve pixel-level forgery localization and image-level forgery detection while maintaining higher detection accuracy and stronger robustness.

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High-Capacity Image Steganography Algorithm Based on Image Style Transfer

Cited by 12 publications

References 34 publications

Designing a Model for Hiding Images in RGB Cover Image Based Scrambling and Encryption Methods

Designing a Model for Hiding Images in RGB Cover Image Based Scrambling and Encryption Methods

Hiding Image within Image Based on Deep Learning

Image Forgery Detection Using Tamper-Guided Dual Self-Attention Network with Multiresolution Hybrid Feature

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