A data hiding scheme based on U-Net and wavelet transform

Liu, Lianshan; Meng, Lingzhuang; Peng, Yanjun; Wang, Xiaoli

doi:10.1016/j.knosys.2021.107022

Cited by 26 publications

(9 citation statements)

References 21 publications

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“…The experiments demonstrated that the method could achieve good results. Liu et al [ 12 ] proposed a data hiding approach based on a newly proposed deep-learning model, U-Net as well as wavelet transform.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, deep learning has shown its power in automatically learning useful and highly abstract features from images [ 9 ]. It also performs well in image steganography [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], which uses an encoding network for steganography and a decoding network for extracting secret information.…”

Section: Introductionmentioning

confidence: 99%

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Deep Image Steganography Using Transformer and Recursive Permutation

Wang

Zhou

Liu

et al. 2022

Entropy

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Image steganography, which usually hides a small image (hidden image or secret image) in a large image (carrier) so that the crackers cannot feel the existence of the hidden image in the carrier, has become a hot topic in the community of image security. Recent deep-learning techniques have promoted image steganography to a new stage. To improve the performance of steganography, this paper proposes a novel scheme that uses the Transformer for feature extraction in steganography. In addition, an image encryption algorithm using recursive permutation is proposed to further enhance the security of secret images. We conduct extensive experiments to demonstrate the effectiveness of the proposed scheme. We reveal that the Transformer is superior to the compared state-of-the-art deep-learning models in feature extraction for steganography. In addition, the proposed image encryption algorithm has good attributes for image security, which further enhances the performance of the proposed scheme of steganography.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Image Steganography Using Transformer and Recursive Permutation

Wang

Zhou

Liu

et al. 2022

Entropy

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“…Recently, some researchers have applied neural networks to steganography [35][36][37][38][39][40][41][42][43][44][45]. As a cooperative algorithm, convolutional neural network with deep supervision edge detector retains more edge pixels over conventional edge detection, so it can increase data hiding capacity [35].…”

Section: Fig 1 a Simple Model Of Information Hidingmentioning

confidence: 99%

“…As a cooperative algorithm, convolutional neural network with deep supervision edge detector retains more edge pixels over conventional edge detection, so it can increase data hiding capacity [35]. In [36], the researchers combined the advantages of U-Net in image detail feature processing and the ability of wavelet transform to divide image details. To improve the capability of resisting steganalysis algorithms, an image steganography scheme based on neural network to transfer image is proposed [37].…”

Section: Fig 1 a Simple Model Of Information Hidingmentioning

confidence: 99%

Large-Capacity Image Data Hiding based on Table Look-up

Ding¹,

Zhang²,

Reulke³

et al. 2021

Preprint

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In previous data hiding techniques, binary rules are usually used to guide the fine-tuning of the values of basic objects in the host media to hide bit 0 and bit 1. In this paper, we propose a new data hiding technique for gray images based on querying a 256x256 information table. The information table is constructed by cloning a 3x3 basic block, which we call seed block. Eight unsigned integer values between 0 and 7, i.e., 3 bit binary data, are assigned to different elements of the seed block. Each time, a pair of pixels are chosen from a host image, and their pixel values are used as row and column numbers to look up the information table. If element value obtained by looking up the table is equal to the 3 bit binary data to be hidden, the values of the pixel pair will remain unchanged. Otherwise, take this element as the central point, we call it the focus element, to enclose a 3x3 window in the information table. Then in the window, find the element which is equal to the data to be hidden. Finally, update the pixel values of the pair with the row and column numbers of the found element in the window. Since the row and column numbers are in the range of 0-255, the updated pixel values will not overflow. In the proposed algorithm, a pair of pixels can hide 3 bits of information, so the embedding capacity is very high. Since the adjustment of pixel values is constrained in a 3x3 window, the modification amount of pixel values is small. The proposed technique belongs to fragile digital watermarking, so it can be used for image authentication and tamper localization. By the evaluation of data hiding capacity, security, imperceptibility, computational cost and extensibility, this algorithm is superior to existing information hiding techniques. The proposed technique can also be used in color image and audio data hiding.

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“…Baluja et al [28] trained a neural network to hide a full-color image within another image of the same size, the watermarked image has a very good visual effect, and the algorithm can embed not only images of different sizes but also text and audio. Meng et al [29] and Liu et al [30] developed an irreversible watermarking scheme using wavelet transform and U-net based machine learning method. Fang et al [31] proposed an information hiding technique based on adversarial generative networks to effectively secure data in data sharing.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Watermarking Algorithm for Medical Images Using Inception V3燼nd燚CT

Yang¹,

Li²,

Bhatti³

et al. 2023

Computers, Materials &Amp; Continua

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Medical images are a critical component of the diagnostic process for clinicians. Although the quality of medical photographs is essential to the accuracy of a physician's diagnosis, they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images. Traditional watermark embedding algorithm embeds the watermark information into the medical image, which reduces the quality of the medical image and affects the physicians' judgment of patient diagnosis. In addition, watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed. This paper proposes a novel watermarking algorithm using the convolutional neural networks (CNN) Inception V3 and the discrete cosine transform (DCT) to address above mentioned problems. First, the medical image is input into the Inception V3 network, which has been structured by adjusting parameters, such as the size of the convolution kernels and the typical architecture of the convolution modules. Second, the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical image. At last, the watermarks are encrypted using the logistic map system and hash function, and the keys are stored by a third party. The encrypted watermarks and the original image features are performed logical operations to realize the embedding of zero-watermark. In the experimental section, multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed algorithms. Our NC values for all the images are more than 90% accurate which shows the robustness of the algorithm. Extensive experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

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A data hiding scheme based on U-Net and wavelet transform

Cited by 26 publications

References 21 publications

Deep Image Steganography Using Transformer and Recursive Permutation

Deep Image Steganography Using Transformer and Recursive Permutation

Large-Capacity Image Data Hiding based on Table Look-up

A Multi-Watermarking Algorithm for Medical Images Using Inception V3燼nd燚CT

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