Cover Selection for Steganography Using Image Similarity

Wang, Zichi; Feng, Guorui; Shen, Liquan; Zhang, Xinpeng

doi:10.1109/tdsc.2022.3181039

Cited by 9 publications

(15 citation statements)

References 47 publications

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“…The use of SRM features to measure the security of various cover selection strategies is highlighted for clarity. The strategies under comparison encompass joint cover selection, minimum distortion selection, and the similarity-based strategy outlined in literature 24 , literature 13 , and literature 14 . The experimental procedure is as follows: We crafted the vector selection in this study through a hybrid training approach involving pre-trained SRNet.…”

Section: Resultsmentioning

confidence: 99%

“…Conversely, acquiring a steganalysis pre-trained model is straightforward and facilitates real-time image analysis. Moreover, Wang et al 14 have provided evidence of the impracticality of their technique when handling multi-image stego scenarios with varying embedding rates and steganography algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…In another work, Wang et al 14 proposed a novel image selection strategy for steganography grounded in SVD (singular value decomposition). They calculated the similarity between images using the SVD coefficients, subsequently integrating the similarity and the embedding distortion to identify the most suitable image.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, the focus in cover selection shifted to measuring image texture complexity 11 , 12 . Additionally, a combination of embedded distortion with either processed distortion 13 or image similarity 14 has been suggested for selecting cover images. However, despite these advancements, these methods remain empirical and fail to establish a direct link between carriers and their security.…”

Section: Introductionmentioning

confidence: 99%

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Security-oriented steganographic payload allocation for multi-remote sensing images

Wu,

Hu,

Liu

2024

Sci Rep

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Multi-image steganography, a technique for concealing information within multiple carrier mediums, finds remote sensing images to be particularly apt carriers due to their complex structures and abundant texture data. These characteristics bolster the resilience against steganalysis and enhance steganographic capacity. The efficacy of multi-image steganography hinges on the diplomatic strategy of cover selection and the meticulous allocation of the payload. Nevertheless, the majority of current methods, which are empirically formulated, predominantly focus on the texture complexity of individual images, thereby potentially undermining overall security. This paper introduces a security-oriented approach to steganographic payload allocation for multiple remote sensing images aimed at fortifying the security of multi-image steganography. Our primary contributions include employing a steganalysis pre-trained network to quantify texture complexity in remote sensing cover images, directly correlating it with security. Additionally, we have developed an adaptive payload allocation strategy for multiple images, which embeds a payload proximate to each image’s maximal steganographic capacity while concurrently ensuring the security of the embedding process. Experimental results corroborate that our methodology excels in cover selection and payload allocation and achieves better undetectability against modern steganalysis tools.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Security-oriented steganographic payload allocation for multi-remote sensing images

Wu,

Hu,

Liu

2024

Sci Rep

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“…Therefore, the data protection issue has become a special concern of academia and industry. There are various schemes to protect data security, such as data hiding [1], [2], encryption [3], [4], hashing [5]- [7] and secret sharing (SS) [8]- [11]. As significant data expressions, digital images can be applied to many applications, such as photography, military, medicine, and legal forensics.…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding in Encrypted Images With Secret Sharing and Hybrid Coding

Zhang

Qin

Tang

2023

IEEE Trans. Circuits Syst. Video Technol.

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Reversible data hiding in encrypted images (RD-HEI) is an essential data security technique. Most RDHEI methods cannot perform well in embedding capacity and security. To address this issue, we propose a new RDHEI method using Chinese remainder theorem-based secret sharing (CRTSS) and hybrid coding. Specifically, a hybrid coding is first proposed for RDH to achieve high embedding capacity. At the content owner side, a novel iterative encryption is designed to conduct block based encryption for perfectly preserving the spatial correlation of original blocks in their encrypted blocks. Then, the CRTSS with the constraints is exploited to generate multiple encrypted image shares, in which spatial correlations of the encrypted blocks are also preserved. Meanwhile, the CRTSS provides good security properties for the proposed method. Since there are strong spatial correlations in the blocks of each share, the datahider can exploit the proposed hybrid coding to perform data embedding for improving capacity. On the receiver side, even if some shares are corrupted/missing, the original image can be losslessly recovered as long as enough uncorrupted marked shares are obtained. Experiment results show that the proposed RDHEI method outperforms some state-of-the-art methods, including some secret sharing (SS) based methods in terms of embedding capacity.

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