Application of a generalized difference expansion based reversible audio data hiding algorithm

Choi, Ka-Cheng; Pun, Chi‐Man; Chen, C. L.

doi:10.1007/s11042-013-1732-1

Cited by 11 publications

(10 citation statements)

References 8 publications

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“…(6) In this paper, the generalized difference expansion algorithm [ 20 ] and LSB algorithm [ 21 ] are applied to embed the recovery watermark produced by homogenous and non-homogenous image blocks into the corresponding I i , respectively.…”

Section: Algorithm Designmentioning

confidence: 99%

Self-recovery reversible image watermarking algorithm

et al. 2018

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The integrity of image content is essential, although most watermarking algorithms can achieve image authentication but not automatically repair damaged areas or restore the original image. In this paper, a self-recovery reversible image watermarking algorithm is proposed to recover the tampered areas effectively. First of all, the original image is divided into homogeneous blocks and non-homogeneous blocks through multi-scale decomposition, and the feature information of each block is calculated as the recovery watermark. Then, the original image is divided into 4×4 non-overlapping blocks classified into smooth blocks and texture blocks according to image textures. Finally, the recovery watermark generated by homogeneous blocks and error-correcting codes is embedded into the corresponding smooth block by mapping; watermark information generated by non-homogeneous blocks and error-correcting codes is embedded into the corresponding non-embedded smooth block and the texture block via mapping. The correlation attack is detected by invariant moments when the watermarked image is attacked. To determine whether a sub-block has been tampered with, its feature is calculated and the recovery watermark is extracted from the corresponding block. If the image has been tampered with, it can be recovered. The experimental results show that the proposed algorithm can effectively recover the tampered areas with high accuracy and high quality. The algorithm is characterized by sound visual quality and excellent image restoration.

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Section: Algorithm Designmentioning

confidence: 99%

Self-recovery reversible image watermarking algorithm

et al. 2018

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“…They improved this method by building a new equation that developed both the visual quality of the stego image and the secret message capacity. Next, Choi et al [20] have presented an application of a generalized difference expansion-based reversible audio data hiding algorithm by using the intelligent partitioning algorithm. This method has exploited the schemes which are used to handle an 8 bit 2D image as the cover.…”

Section: Related Workmentioning

confidence: 99%

Audio Data Hiding Based on Sample Value Modification Using Modulus Function

2016

J Inf Process Syst

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Data hiding is a wide field that is helpful to secure network communications. It is common that many data hiding researchers consider improving and increasing many aspects such as capacity, stego file quality, or robustness. In this paper, we use an audio file as a cover and propose a reversible steganographic method that is modifying the sample values using modulus function in order to make the reminder of that particular value to be same as the secret bit that is needed to be embedded. In addition, we use a location map that locates these modified sample values. This is because in reversible data hiding it needs to exactly recover both the secret message and the original audio file from that stego file. The experimental results show that, this method (measured by correlation algorithm) is able to retrieve exactly the same secret message and audio file. Moreover, it has made a significant improvement in terms of the following: the capacity since each sample value is carrying a secret bit. The quality measured by peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), Pearson correlation coefficient (PCC), and Similarity Index Modulation (SIM). All of them have proven that the quality of the stego audio is relatively high.

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“…embedding capacity with various methods such as histogram shifting [18,19], generalized difference expansion [20], and adaptive embedding [21]. The second is that the quality of the stego file generated by the embedding process should be as high as possible.…”

Section: Introductionmentioning

confidence: 99%

“…We believe that even though it is less popular than image and video [11], audio has the potential for exploration according to its characteristics. The proposed method is developed based on Prediction Error Expansion (PEE) [20,29], where their capacity per sample is relatively low, and so is its corresponding stego quality. The proposed improvement is, in general, designed by dynamically assigning and spreading the payload to every sample, and multiple mirror embedding to maintain the capacity and quality, as well as the reversibility.…”

Section: Introductionmentioning

confidence: 99%