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Enhancing LSB embedding schemes using chaotic maps systems

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Abstract

In our modern life, persons and institutions alike are rapidly embracing the shift toward communication via the Internet. As these entities adopt a faster and efficient communication protocol, information security techniques such as steganography and cryptography become powerful and necessary tools for conducting secure and secrecy communications. Currently, several steganography techniques have been developed, and the least significant bit (LSB) is one of these techniques which is a popular type of steganographic algorithms in the spatial domain. Indeed, as any other existing techniques, the selection of positions for data embedding within a cover signal mainly depends on a pseudorandom number generator without considering the relationship between the LSBs of the cover signal and the embedded data. In this paper and for best pixels’ positions adjustment, in which the visual distortion of the stego-image, as well as the embedding changes, becomes optimum, we propose two new position selection scenarios of LSBs-based steganography. Our new works are to improve the embedding efficiency, that is to say, select the suitable cover image pixels’ values that optimize the expected number of modifications per pixel and the visual distortion.

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Acknowledgements

The authors are grateful to the anonymous referees for their valuable and helpful comments. This research has been carried out within the PRFU project (Grant: A01L08UN120120180001) of the Department of Electrical Engineering, University of Larbi Tebessi, Tebessa. The authors thank the staff of LAMIS laboratory for helpful comments and suggestions.

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  1. Laboratory of Mathematics, Informatics and Systems (LAMIS), University of Larbi Tebessi, Tebessa, Algeria

    Lakhdar Laimeche, Abdallah Meraoumia & Hakim Bendjenna

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  1. Lakhdar Laimeche
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  2. Abdallah Meraoumia
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  3. Hakim Bendjenna
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Correspondence to Lakhdar Laimeche.

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Laimeche, L., Meraoumia, A. & Bendjenna, H. Enhancing LSB embedding schemes using chaotic maps systems. Neural Comput & Applic 32, 16605–16623 (2020). https://doi.org/10.1007/s00521-019-04523-z

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