Abstract
Data hiding plays an important role in privacy protection and authentication, but most data hiding methods fail to achieve satisfactory performance in resisting scaling attacks and additive attacks. To this end, this paper proposes a new quantization index modulation (QIM) variant based on division domains (D-QIM). It can not only resist the above two attacks well, but also adjust the performance trade-offs by controlling the parameters. Simulation results confirm the performance gain of D-QIM in terms of the bit error rate (BER).
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (61902149, 61932010 and 62032009) and the Natural Science Foundation of Guangdong Province (2020A1515010393).
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Yu, S., Qin, J., Deng, J., Lyu, S., Li, F. (2022). Data Hiding in the Division Domain: Simultaneously Achieving Robustness to Scaling and Additive Attacks. In: Wang, Y., Zhu, G., Han, Q., Zhang, L., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1629. Springer, Singapore. https://doi.org/10.1007/978-981-19-5209-8_4
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DOI: https://doi.org/10.1007/978-981-19-5209-8_4
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