An adaptive reversible watermarking in IWT domain

Meng, Lingzhuang; Liu, Lianshan; Tian, Gang; Wang, Xiaoli

doi:10.1007/s11042-020-09686-9

Cited by 27 publications

(15 citation statements)

References 44 publications

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“…( 14). The masking of 𝑧 sequence and 𝑑 3 is done to construct 𝑑 4 in such a way that whenever 𝑤 𝑖 𝑤𝑖𝑙𝑙 𝑏𝑒𝑐𝑜𝑚𝑒 1 , c 𝑑 𝑖 3 will be complemented to get 𝑑 𝑖 4 . Otherwise, no change occurs.…”

Section: Encryption and Decryptionmentioning

confidence: 99%

“…Information hiding is the art of protecting the information in a secured manner. Steganography and watermarking methods are two main categories of Information hiding [3,4]. The watermarking is considered a process of making information imperceptible, where it changes a work to embed information related to the particular work.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 4. The quantitative analysis of the proposed reversible watermarking method for a medical dataset in terms of PSNR…”

mentioning

confidence: 99%

See 2 more Smart Citations

Reversible Watermarking Using Hyperchaotic System for Secure Image Transmission

2021

IJIES

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In telemedicine applications, the images pertaining to medicines and diseases can undergo tampering and forging, where it increases risk in diagnosis. The reversible watermarking technique is capable to extract the embedded information and retrieve the original images completely from watermarked images. It has some specific features like lower distortion of images and high capacity of payload. To solve such an issue, a reversible watermarking method is proposed to provide security for the medical images using hyperchaotic with Deoxyribonucleic Acid (DNA) sequence. The proposed reversible watermarking method utilized standard images (lena, Barbara, boat and medical images) for encryption and decryption. Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) are used to compress the cover images and to decompress the stego images. Further, the thresholding with morphological transform is employed for segmentation of Region of Interest (RoI) in secret images. In addition, the encryption and decryption of secret image is done by hyperchaotic system with DNA sequence. Lastly, the embedding and extraction of secret images and cover images from the stego images is performed by reversible watermarking with differential expansion and modulus function. Simulation result shows that the proposed reversible watermarking method achieved Peak Signal to Noise Ratio (PSNR) of 99 dB for secret image construction, which is better compared to other traditional watermarking techniques.

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Section: Encryption and Decryptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reversible Watermarking Using Hyperchaotic System for Secure Image Transmission

2021

IJIES

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“…e transform domain scheme used a series of transforms to convert image into transform coefficients and then used the correlation between the coefficients for information hiding. More researched schemes included discrete cosine transform, wavelet transform [5,6], and so on. In addition, in order to improve the secrecy and robustness of hidden information, information hiding schemes based on image compression [7] and encryption [8] have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

A Novel High-Capacity Information Hiding Scheme Based on Improved U-Net

Liu

Meng

Zheng

et al. 2022

Security and Communication Networks

Self Cite

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With the gradual introduction of deep learning into the field of information hiding, the capacity of information hiding has been greatly improved. Therefore, a solution with a higher capacity and a good visual effect had become the current research goal. A novel high-capacity information hiding scheme based on improved U-Net was proposed in this paper, which combined improved U-Net network and multiscale image analysis to carry out high-capacity information hiding. The proposed improved U-Net structure had a smaller network scale and could be used in both information hiding and information extraction. In the information hiding network, the secret image was decomposed into wavelet components through wavelet transform, and the wavelet components were hidden into image. In the extraction network, the features of the hidden image were extracted into four components, and the extracted secret image was obtained. Both the hiding network and the extraction network of this scheme used the improved U-Net structure, which preserved the details of the carrier image and the secret image to the greatest extent. The simulation experiment had shown that the capacity of this scheme was greatly improved than that of the traditional scheme, and the visual effect was good. And compared with the existing similar solution, the network size has been reduced by nearly 60%, and the processing speed has been increased by 20%. The image effect after hiding the information was improved, and the PSNR between the secret image and the extracted image was improved by 6.3 dB.

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“…All the positions and strengths of hiding information as well as the hiding domain are automatically achieved by training the networks. However, these works have one or more of the following shortcomings: (1) the colors of the generated steganographic images are distorted [18]; (2) the hiding capacity is limited in [15,[18][19][20][21]. (3) the model [15][16][17]22] does not fit the steganographic process well; and (4) the secret image is not encrypted and then steganographic in [16,21,23]; thus, it is not sufficiently secure.…”

Section: Introductionmentioning

confidence: 99%

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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An adaptive reversible watermarking in IWT domain

Cited by 27 publications

References 44 publications

Reversible Watermarking Using Hyperchaotic System for Secure Image Transmission

Reversible Watermarking Using Hyperchaotic System for Secure Image Transmission

A Novel High-Capacity Information Hiding Scheme Based on Improved U-Net

Deep Image Steganography Using Transformer and Recursive Permutation

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