网络与信息安全学报 ›› 2021, Vol. 7 ›› Issue (6): 1-10.doi: 10.11959/j.issn.2096-109x.2021102
• 综述 • 下一篇
王垚飞1,2, 张卫明1,2, 陈可江1,2, 周文柏1,2, 俞能海1,2
修回日期:
2021-10-28
出版日期:
2021-12-15
发布日期:
2021-12-01
作者简介:
王垚飞(1996− ),男,河北邯郸人,中国科学技术大学博士生,主要研究方向为信息隐藏基金资助:
Yaofei WANG1,2, Weiming ZHANG1,2, Kejiang CHEN1,2, Wenbo ZHOU1,2, Nenghai YU1,2
Revised:
2021-10-28
Online:
2021-12-15
Published:
2021-12-01
Supported by:
摘要:
图像非加性隐写不仅能更好地维持图像元素的分布,而且具有较高的抗检测性能。首先对图像非加性隐写方法进行了梳理,将其分为两大类:非加性失真设计和非加性隐写编码设计。进一步将非加性失真设计总结为3类:基于理论模型、基于修改原则和基于对抗检测的方法,对这些方法进行了对比。最后分析了非加性隐写面临的困难问题和未来的发展思路。
中图分类号:
王垚飞, 张卫明, 陈可江, 周文柏, 俞能海. 图像非加性隐写综述[J]. 网络与信息安全学报, 2021, 7(6): 1-10.
Yaofei WANG, Weiming ZHANG, Kejiang CHEN, Wenbo ZHOU, Nenghai YU. Survey on image non-additive steganography[J]. Chinese Journal of Network and Information Security, 2021, 7(6): 1-10.
表1
针对灰度空域图像的不同非加性失真定义方法对比Table 1 Comparison of different methods of defining non-additive distortion for grayscale spatial domain images"
方法 | 性能 | 性能比较 | 特点 | 缺点 |
Gibbs[ | 相比于加性失真略有提升,如在BOWS2[ | ASYMM>CMD>Synch>GMRF>H UGO-BD>Gibbs | 基于Gibbs构造具有一定的理论性,为非加性方法定义提供了参考 | 安全性提升较少,实行比较复杂 |
HUGO-BD[ | 相比于加性失真有较大提升,如在BOWS2中采用二元嵌入,嵌入率为0.4 bpp,抵抗二阶 SPAM 特征的性能可以提升约0.16 | 采用模型矫正,通过动态更新元素±1的失真,在一定限度上克服了加性模型的局限 | 仅适用于二元嵌入,且难以抵抗基于富模型特征的隐写分析 | |
CMD[ | 相比于加性失真有较大提升,如在BOSSBase[ | 基于方向一致性原则,通过更新失真的方式鼓励相邻像素的修改方向同向修改,方法简单有效,可以和任意初始失真进行组合 | 基于启发式,缺乏一定的理论性 | |
Synch[ | 相比于加性失真有部分提升,如在BOSSBase 中采用三元嵌入,嵌入率为0.4 bpp,以HILL为初始失真抵抗SRM特征的性能可以提升约0.03 | 基于方向一致性原则,简单有一定效果,可以和任意初始失真进行组合 | 基于启发式,缺乏理论性,弱于CMD | |
ASYMM[ | 抗监测性能相比于CMD有微弱的提升 | 基于非对称模型,分块嵌入,将相邻嵌入作为先验对待嵌入像素的修改率进行优化 | 抗检测性能提升微弱,计算复杂度高 | |
GMRF[ | 略好于MiPOD[ | 基于四元素交叉邻域的高斯马尔可夫随机场模型来描述载体图像的局部元素之间的相互作用,将隐写问题表述为与模型相关的载体载秘KL最小化问题 | 计算复杂度高,考虑相关性带来的提升较小 | |
注:嵌入率(bpp,bits per pixel) |
表2
针对彩色空域图像的不同非加性失真定义方法对比Table 2 Comparison of different methods of defining non-additive distortion for color spatialdomain images"
方法 | 性能 | 性能比较 | 特点 | 缺点 |
CMD-C[ | 相比加性失真有所提升,如 在BOSSbasePPGBIC中采用三元嵌入,嵌入率为 0.4bpcp,以 HILL 为初始失真抵抗SCRMQ1[ | GINA>CPV-CMD>CMD-C>CPV | 基于方向一致性原则,通过更新失真的方式不加区分地鼓励通道内和通道间的修改方向一致,简单有一定效果,可以和任意初始失真进行组合 | 没有挖掘彩色通道间的相关性和差异性,缺乏理论性,部分情况弱于基于CMD的嵌入 |
CPV[ | 相 比 加 性 失 真 有 所 提 升,如 在BOSSbasePPGBIC中采用三元嵌入,嵌入率为0.4bpcp,相比于HILL抵抗SCRMQ1特征的性能可以提升约 0.007,和 CMD结合后CPV-CMD可进一步提升0.072 | 一种基于颜色像素向量的失真定义,通过定义3个通道的相同位置处的共同失真,并采用失真分解的方式获得单个像素的失真,可以在3个通道中自适应分配消息 | 没有完全考虑到通道间的相关性,失真计算复杂度较高 | |
GINA[ | 相比 CMD-C 和 CMD 有所提升,如在BOSSbasePPGBIC中采用三元嵌入,嵌入率为 0.4bpcp,以 HILL 为初始失真抵抗SCRMQ1特征的性能可以提升约0.10 | 挖掘了彩色通道间的相关性和差异性,权衡方向一致性原则和复杂度优先原则,简单有效,可以和任意初始失真进行组合 | 缺乏理论性,参数设计仅基于启发式获得 | |
注:嵌入率(bpcp,bits per color pixel) |
表3
针对灰度JPEG图像的不同非加性失真定义方法对比Table 3 Comparison of different methods of defining non-additive distortion for grayscale JPEG images"
方法 | 性能 | 性能比较 | 特点 | 缺点 |
BBC[ | 相比于加性失真有所提升,如在质量因子为75的 BOSSBase 中采用三元嵌入,嵌入率为0.5bpnzac,以UERD[ | BAR>BBC++>Cov>BBM>BBC | 首个针对 JPEG 图像的非加性失真定义原则,通过定义相邻块相同位置的一对DCT系数的联合失真来鼓励修改后的空域块边界连续,考虑相邻块DCT系数的相关性 | DCT 系数之间的相关性考虑不完备,效果提升较少 |
BBC++[ | 相比于 BBC 有所提升,如在质量因子为 75的 BOSSBase 中采用三元嵌入,嵌入率为0.5bpnzac,以 UERD 为基础失真抵抗 GFR特征的性能可以提升0.040。 | BBC方案的增强版,通过分块嵌入,嵌入过程中更新载体和失真来维持空域块边界连续性,相较于BBC具有更明显的提升性能 | 仅考虑了块间系数的相关性 | |
BBM[ | 相比于加性失真有所提升,如在质量因子为75的BOSSBase中采用三元嵌入,嵌入率为0.5bpnzac,以 UERD 为基础失真抵抗 GFR特征的性能可以提升0.030 | 首个考虑DCT块间相关性的非加性失真定义原则,通过失真更新的方式,减少空域块边界修改扰动,有一定的效果 | 仅考虑了块内系数的相关性 | |
BAR[ | 相比于加性失真有所提升,如在质量因子为75的BOSSBase中采用三元嵌入,嵌入率为0.5bpnzac,以 UERD 为基础失真抵抗 GFR特征的性能可以提升0.040 | 同时考虑了DCT块内和块间系数的相关性,通过失真更新的方式,维持空域块边界的分布,效果提升显著 | 基于启发式设计,参数基于实验调节 | |
Cov[ | 相比于加性失真性能有一定的提升,如在质量因子为75的BOSSBase中采用三元嵌入,嵌入率为0.28bpnzac,以UERD为基础失真抵抗DCTR[ | 在图像处理流程中利用DCT系数间的统计分析设计同步策略,对相邻块构建协方差矩阵,将每个系数相关的经验失真转化为高斯分布 | 计算复杂度高,考虑到相关性带来的提升较小 | |
注:嵌入率(bpnzac,bits per non-zero ac coefficients) |
表4
基于深度学习的不同非加性失真定义方法对比Table 4 Comparison of different non-additive distortion definition methods based on deep learning"
方法 | 性能 | 性能比较 | 特点 | 缺点 |
ADV-EMB[ | 对于已知隐写分析网络,该方法有较高的抗检测性能,在已知隐写分析且无防御情况下,有较高的漏检率 | Backpack>min-max>MCTSteg>ADV-EMB | 首个利用对抗思想动态调整失真的对抗嵌入,即先嵌入一部分,另一部分根据梯度来修改失真进行嵌入,在白盒情况下有较高的安全性能 | 攻击成功率较低,迁移性弱 |
min-max策略[ | 一种对抗训练策略,一定限度上增强了ADV-EMB的迁移性 | 一种通用的对抗训练策略,具有较高的迁移性 | 训练代价较高 | |
MCTSteg[ | 空域和频域相比 CMD、BBC 和BBM都有提升 | 一种通用的非加性失真定义方法,采用分块嵌入,通过强化学习计算待嵌入块的最优修改方式后更新失真再嵌入 | 计算复杂度高 | |
Backpack[ | 相比ADV-EMB性能有较大的提升 | 基于理论模型,通过Gumbel-Softmax分布的样本来逼近离散的嵌入变化,利用梯度信息来求解失真更新,具有较好的性能 |
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