CLC number: TP309
On-line Access: 2023-08-29
Received: 2023-01-20
Revision Accepted: 2023-08-29
Crosschecked: 2023-02-28
Cited: 0
Clicked: 714
Citations: Bibtex RefMan EndNote GB/T7714
Linna ZHOU, Zhigao LU, Weike YOU, Xiaofei FANG. Reversible data hiding using a transformer predictor and an adaptive embedding strategy[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2300041 @article{title="Reversible data hiding using a transformer predictor and an adaptive embedding strategy", %0 Journal Article TY - JOUR
基于transformer和自适应嵌入策略的可逆信息隐藏1北京邮电大学网络空间安全学院,中国北京市,100084 2国际关系学院网络空间安全学院,中国北京市,100091 摘要:在可逆信息隐藏(RDH)领域中,设计高精度预测器以减少嵌入失真和开发有效的嵌入策略以最小化由嵌入信息引起的失真是提高RDH性能的两个关键方面。本文提出一种新的RDH方法,包括基于transformer的预测器和具有多个嵌入规则的新嵌入策略。在预测器部分,我们首先设计了一个基于transformer的预测器。然后,提出一种图像分割方法,将图像分成4部分,可以使用更多的像素作为上下文。与其他预测器相比,我们的预测器可以将用于预测的像素范围从相邻像素扩展到全局像素,从而使其在减少嵌入失真方面更为准确。在嵌入策略部分,我们首先提出了能够利用目标块中像素的复杂性度量。然后,开发了一种改进的预测误差排序规则。最后,我们首次提出一种包含多个嵌入规则的嵌入策略。本文中的RDH方法可以有效减少失真,同时在提高隐藏图像的视觉质量方面提供令人满意的结果。实验结果表明,本文中提出的RDH算法的性能处于领先地位。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Chen M, Chen ZY, Zeng X, et al., 2010. Model order selection in reversible image watermarking. IEEE J Sel Top Signal Process, 4(3):592-604. [2]Chen M, Radford A, Child R, et al., 2020. Generative pretraining from pixels. Proc 37th Int Conf on Machine Learning, Article 158. [3]Coltuc D, 2011. Improved embedding for prediction-based reversible watermarking. IEEE Trans Inform Forens Secur, 6(3):873-882. [4]Coltuc D, 2012. Low distortion transform for reversible watermarking. IEEE Trans Image Process, 21(1):412-417. [5]Cox IJ, Miller ML, Bloom JA, 2002. Digital Watermarking. Morgan Kaufmann, San Francisco, USA. [6]Dosovitskiy A, Beyer L, Kolesnikov A, et al., 2021. An image is worth 16×16 words: transformers for image recognition at scale. Proc Int Conf on Learning Representations. [7]Dragoi IC, Caciula I, Coltuc D, 2018. Improved pairwise pixel-value-ordering for high-fidelity reversible data hiding. Proc 25th IEEE Int Conf on Image Processing, p.1668-1672. [8]Esser P, Rombach R, Ommer B, 2021. Taming transformers for high-resolution image synthesis. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.12873-12883. [9]Goodfellow I, Pouget-Abadie J, Mirza M, et al., 2014. Generative adversarial nets. Proc 27th Int Conf on Neural Information Processing Systems, p.2672-2680. [10]He WG, Cai ZC, 2021. Reversible data hiding based on dual pairwise prediction-error expansion. IEEE Trans Image Process, 30:5045-5055. [11]He WG, Cai J, Zhou K, et al., 2017. Efficient PVO-based reversible data hiding using multistage blocking and prediction accuracy matrix. J Vis Commun Image Represent, 46:58-69. [12]Hong W, 2012. Adaptive reversible data hiding method based on error energy control and histogram shifting. Opt Commun, 285(2):101-108. [13]Howard PG, Vitter JS, 2016. Arithmetic coding for data compression. In: Kao MY (Ed.), Encyclopedia of Algorithms. Springer, New York, USA, p.145-150. [14]Hu RW, Xiang SJ, 2021. CNN prediction based reversible data hiding. IEEE Signal Process Lett, 28:464-468. [15]Hu RW, Xiang SJ, 2022. Reversible data hiding by using CNN prediction and adaptive embedding. IEEE Trans Patt Anal Mach Intell, 44(12):10196-10208. [16]Jafar IF, Darabkh KA, Al-Zubi RT, et al., 2016. Efficient reversible data hiding using multiple predictors. Comput J, 59(3):423-438. [17]Karras T, Aila T, Laine S, et al., 2018. Progressive growing of GANs for improved quality, stability, and variation. Proc 6th Int Conf on Learning Representations. [18]Karras T, Laine S, Aila T, 2019. A style-based generator architecture for generative adversarial networks. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.4401-4410. [19]Li XL, Yang B, Zeng TY, 2011. Efficient reversible water- marking based on adaptive prediction error expansion and pixel selection. IEEE Trans Image Process, 20(12):3524-3533. [20]Li XL, Li J, Li B, et al., 2013. High-fidelity reversible data hiding scheme based on pixel-value-ordering and prediction error expansion. Signal Process, 93(1):198-205. [21]Liu ZW, Luo P, Wang XG, et al., 2015. Deep learning face attributes in the wild. Proc IEEE Int Conf on Computer Vision, p.3730-3738. [22]Luo LX, Chen ZY, Chen M, et al., 2010. Reversible image watermarking using interpolation technique. IEEE Trans Inform Forens Secur, 5(1):187-193. [23]Ou B, Li XL, Zhao Y, et al., 2013. Pairwise prediction error expansion for efficient reversible data hiding. IEEE Trans Image Process, 22(12):5010-5021. [24]Ou B, Li XL, Zhao Y, et al., 2014. Reversible data hiding using invariant pixel-value-ordering and prediction error expansion. Signal Process Image Commun, 29(7):760-772. [25]Ou B, Li XL, Wang JW, 2016. High-fidelity reversible data hiding based on pixel-value-ordering and pairwise prediction error expansion. J Vis Commun Image Represent, 39:12-23. [26]Peng F, Li XL, Yang B, 2014. Improved PVO-based reversible data hiding. Dig Signal Process, 25:255-265. [27]Qu XC, Kim HJ, 2015. Pixel-based pixel value ordering predictor for high-fidelity reversible data hiding. Signal Process, 111:249-260. [28]Russakovsky O, Deng J, Su H, et al., 2015. ImageNet large scale visual recognition challenge. Int J Comput Vis, 115(3):211-252. [29]Sachnev V, Kim HJ, Nam J, et al., 2009. Reversible watermarking algorithm using sorting and prediction. IEEE Trans Circ Syst Video Technol, 19(7):989-999. [30]Thodi DM, Rodriguez JJ, 2007. Expansion embedding techniques for reversible watermarking. IEEE Trans Image Process, 16(3):721-730. [31]Tian J, 2003. Reversible data embedding using a difference expansion. IEEE Trans Circ Syst Video Technol, 13(8):890-896. [32]van den Oord A, Vinyals O, Kavukcuoglu K, 2017. Neural discrete representation learning. Proc 31st Int Conf on Neural Information Processing Systems, p.6309-6318. [33]Vaswani A, Shazeer N, Parmar N, et al., 2017. Attention is all you need. Proc 31st Int Conf on Neural Information Processing Systems, p.6000-6010. [34]Wang X, Ding J, Pei QQ, 2015. A novel reversible image data hiding scheme based on pixel value ordering and dynamic pixel block partition. Inform Sci, 310:16-35. [35]Wang XY, Wang XY, Ma B, et al., 2021. High precision error prediction algorithm based on ridge regression predictor for reversible data hiding. IEEE Signal Process Lett, 28:1125-1129. [36]Weng SW, Zhang GH, Pan JS, et al., 2017. Optimal PPVO-based reversible data hiding. J Vis Commun Image Represent, 48:317-328. [37]Weng SW, Shi YQ, Hong W, et al., 2019. Dynamic improved pixel value ordering reversible data hiding. Inform Sci, 489:136-154. [38]Zhang T, Li XL, Qi WF, et al., 2020a. Location-based PVO and adaptive pairwise modification for efficient reversible data hiding. IEEE Trans Inform Forens Secur, 15:2306-2319. [39]Zhang T, Li XL, Qi WF, et al., 2020b. Prediction error value ordering for high-fidelity reversible data hiding. Proc 26th Int Conf on Multimedia Modeling, p.317-328. [40]Zheng CX, Cham TJ, Cai JF, et al., 2022. Bridging global context interactions for high-fidelity image completion. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.11512-11522. [41]Zhou BL, Lapedriza A, Khosla A, et al., 2018. Places: a 10 million image database for scene recognition. IEEE Trans Patt Anal Mach Intell, 40(6):1452-1464. Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE |
Open peer comments: Debate/Discuss/Question/Opinion
<1>