Recent Advances in Information Hiding and Applications by Shuai Yue, Zhi-Hui Wang, Chin-Chen Chang (auth.),

By Shuai Yue, Zhi-Hui Wang, Chin-Chen Chang (auth.), Jeng-Shyang Pan, Hsiang-Cheh Huang, Lakhmi C Jain, Yao Zhao (eds.)

This examine booklet offers a pattern of modern advances in info hiding suggestions and their purposes. It includes:

Image info hiding scheme in line with vector quantization and photo graph coloring

The copyright safeguard method for Android platform

Reversible information hiding

ICA-based photograph and video watermarking

Content-based invariant photograph watermarking

Single bitmap block truncation coding of colour photographs utilizing cat swarm optimization

Genetic-based wavelet packet watermarking for copyright protection

Lossless textual content steganography in compression coding

Fast and low-distortion means acoustic synchronized acoustic-to-acoustic steganography scheme

Video watermarking with shot detection.

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Additional resources for Recent Advances in Information Hiding and Applications

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Tsai et al. [27] presented a lossless data hiding approach using predicted coding and histogram shifting. The prediction technique first explored the similarities of neighboring pixels in the image, after which the residual histogram of the predicted errors of the host image hid the data bits. 5 dB when embedding the same amount of data bits. Chen et al. [4] used the additive prediction-error expansion to embed a large amount data bits into images. Namely, the predictionerror was used to embed a bit ‘1’ or ‘0’ by expanding it additively or leaving it unchanged.

Chen et al. [4] used the additive prediction-error expansion to embed a large amount data bits into images. Namely, the predictionerror was used to embed a bit ‘1’ or ‘0’ by expanding it additively or leaving it unchanged. Simulation showed that the scheme provided competitive performance compared with other existing techniques. Yang and Tsai [28] presented a reversible data hiding method by using interleaving prediction. -Y. -C. Hu transformed into histogram to generate high peak values and improve hiding capacity.

3) Y if W = 1. 5 mod 2 = 1, In this example, signal X is modified into Y such that its quantization index is an even number to imply a binary value ‘0’, and vice versa. 4. The basic idea of relationship-based watermarking is to use two pixel values or transform domain coefficients in an image to represent each bit of a binary watermark. If the first value is larger than the second, then an ‘1’ is encoded; otherwise, a ‘0’ is encoded. Hsu and Wu [8] proposed an approach using middle frequency coefficients chosen from one or more 8 × 8 DCT blocks to embed watermarks.

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