A robust and secure blind color image watermarking scheme based on contourlet transform and Schur decomposition

Hu, Fangxu; Cao, Hongjiao; Chen, Siyu; Sun, Yehan; Su, Qingtang

doi:10.1007/s00371-022-02610-2

Cited by 10 publications

(6 citation statements)

References 34 publications

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“…Poor robustness of geometric attacks Transformation domain NSCT-schur [9] 2022 Good translation invariance Performance of rotation attack was not tested Curvelet-DCT [19] 2020 Good invisibility and robustness of conventional attacks…”

Section: Spatial Domain Lsb-dwt [10] 2022 Good Robustness Of Gaussian...mentioning

confidence: 99%

“…In recent years, medical image watermarking algorithms have become a popular research topic. According to the location of the watermark embedding, it can be divided into spatial domain watermarking and transform domain watermarking [8,9]. Spatial domain watermarking has the advantage of simple operation by directly modifying image pixels for watermark embedding.…”

Section: Introductionmentioning

confidence: 99%

“…Using a single transform domain does not meet the needs of all watermarking algorithms simultaneously, and to compensate for each other's shortcomings, most existing algorithms use hybrid domain techniques. Hu et al [9] used non-subsampling contour transform (NSCT) and Schur decomposition to achieve watermark embedding. The multi-scale and translation invariance of NSCT is fully utilised to improve the effectiveness of translation attacks, but it cannot resist rotation attacks better.…”

Section: Introductionmentioning

confidence: 99%

“…Hu et al. [9] used non‐subsampling contour transform (NSCT) and Schur decomposition to achieve watermark embedding. The multi‐scale and translation invariance of NSCT is fully utilised to improve the effectiveness of translation attacks, but it cannot resist rotation attacks better.…”

Section: Introductionmentioning

confidence: 99%

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Robust zero‐watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

Yuan,

Li,

Liu

et al. 2024

CAAI Trans on Intel Tech

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In the intricate network environment, the secure transmission of medical images faces challenges such as information leakage and malicious tampering, significantly impacting the accuracy of disease diagnoses by medical professionals. To address this problem, the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi‐stage discrete wavelet transform (DWT), Daisy descriptor, and discrete cosine transform (DCT). The algorithm initially encrypts the original medical image through DWT‐DCT and Logistic mapping. Subsequently, a 3‐stage DWT transformation is applied to the encrypted medical image, with the centre point of the LL3 sub‐band within its low‐frequency component serving as the sampling point. The Daisy descriptor matrix for this point is then computed. Finally, a DCT transformation is performed on the Daisy descriptor matrix, and the low‐frequency portion is processed using the perceptual hashing algorithm to generate a 32‐bit binary feature vector for the medical image. This scheme utilises cryptographic knowledge and zero‐watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image, which meets the basic requirements of medical image watermarking. The embedding and extraction of watermarks are accomplished in a mere 0.160 and 0.411s, respectively, with minimal computational overhead. Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks, with a notable performance in resisting rotation attacks.

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Section: Spatial Domain Lsb-dwt [10] 2022 Good Robustness Of Gaussian...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust zero‐watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

Yuan,

Li,

Liu

et al. 2024

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…The embedded secret information bits are obtained according to the partition index values of the output codewords. As an information hiding algorithm in compressed domain, QIM steganography is widely popularized to other various streaming media including speeches, such as, images [35], audios [36], videos [37], etc.. In view of of the QIM-based speech steganography, Xiao et al [29] proposed the complementary neighbor vertices (CNV) QIM-based algorithm for information embedding during the VQ process of LPC for the first time.…”

Section: Introductionmentioning

confidence: 99%

Steganalysis of Compressed Speech Based on Association Rule Mining

Gao

Jie

2022

IEEE Access

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Currently, steganography based on compressed speech streams is gathering more and more attention. Meanwhile, it poses a huge threat to cyber security. As a counter technique, steganalysis can detect whether an illegal secret message is embedded in a compressed speech. To further improve the detection performance of current methods, a novel steganalysis method based on codeword association rule mining (CARM) is proposed in this paper. Firstly, we analyzed the spatiotemporal relationships between codewords in compressed speech. Secondly, the steganography-sensitivity codeword association rule base in training set was built based on the confidence change of codeword association rules before and after steganography. Thirdly, the steganography characteristic index and the corresponding dynamic partition threshold in validation set were computed to determine whether the compressed speech segment contains covert communication or not. Finally, comprehensive experiments were conducted to evaluate the performance of the proposed CARM steganalysis method under various conditions, including different association rule patterns, whether to use dynamic partition threshold, different embedding rates, different speech lengths, etc. The experimental results verify that CARM can achieve better performance than the comparison methods. In addition, the detection accuracy of CARM method can be improved significantly by using dynamic partition threshold at low embedding rates.

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