A review of generative image steganography: methods, robustness challenges and prospects

doi:10.3969/j.issn.1671-1122.2026.07.005

Abstract

Abstract:

Generative image steganography (GIS) embeds secret information into the image generation process, providing a new path different from the traditional carrier modification paradigm. However, the research on this technical method is still challenged by robustness issues. Especially in online social network (OSN), processing such as recompression, format conversion, and multiple rounds of re-encoding can undermine the stability of hidden signals and generated inversion, and intensify the conflict between robustness and capacity-security performance. This paper focused on the main line of robustness and conducted a systematic review of current GIS methods. Firstly, it sorted out the generative steganography methods based on three different technical routes and their recent progress. Subsequently, analyzed the robustness challenges of OSN in lossy channels. Then summarized the robustness evaluation indicators, OSN channel modeling and other experimental evaluation norms. Finally, the future research directions of robust GIS were prospected from multiple aspects such as distorted channel modeling and robust latent space mapping, aiming to deepen the systematic understanding of the robustness problem of GIS in OSN channels and promote related frontier research and applications.

Key words: image steganography, generative artificial intelligence, robustness, online social network

CLC Number:

TP309

Gong Changwei, Jin Bo, Yuan Deyu, Zhang Xuan. A review of generative image steganography: methods, robustness challenges and prospects[J]. Netinfo Security, 2026, 26(7): 1058-1076.

Figures/Tables 9

方法	类型	数据集	载荷	结果	说明
文献[2]方法	GAN	DIV2K	消息嵌入深度1/3/6（约为 0.96/2.36/2.24~ 2.27 bpp）	准确率约为 0.98 / 0.89 / 0.67~0.69	该方法验证了GAN生成式无覆盖隐写的可行性，但随着嵌入深度增加，消息恢复准确率明显下降
文献[3]方法	GAN	FFHQ	1.0/2.0/3.0 bpp	消息比特准确率为97.15%/ 79.62%/ 72.74%；弗雷歇距离（Fréchet Inception Distance，FID）为13.4 / 24.8 / 30.4	GSN能够实现消息驱动的生成式隐写，但载荷升高后恢复准确率和图像质量下降较明显，说明GAN路线在高容量条件下仍存在稳定性不足问题
文献[4]方法	FM	CelebA	3.0/24.0 bpp	3 bpp下准确率为92.44%；24 bpp下准确率为99.53%	GSF利用Glow的可逆映射提高了信息恢复能力，但高容量结果与保存格式关系较大，部分结果不宜直接迁移至OSN有损传播场景
文献[5]方法	FM	CelebA-HQ	0.1~4.0 bpp	0.1~2.0 bpp下准确率为1.0000；4.0 bpp下准确率为0.9943	该方法通过分布保持映射降低含密潜变量与原始先验分布之间的差异，在高容量条件下仍保持较高提取准确率和较好的抗检测性
文献[10]方法	DM	UniStega	图像消息无法用bpp衡量	PSNR为21.248 dB；SSIM为0.711；学习感知图像块相似度为0.320	CRoSS利用Stable Diffusion 实现可控图像级隐写，重点在于秘密图像恢复和生成可控性，而非标准bpp比特流传输
文献[16]方法	DM	UniStega	图像消息无法用bpp衡量	PSNR为23.290 dB；SSIM为0.769；LPIPS为0.266	DiffStega在统一复现实验中相比CRoSS具有更高秘密图像恢复质量，并在JPEG压缩后保持更好的恢复效果
文献[18]方法	DM	FFHQ	1.0~6.0 bpp	载荷从1.0 bpp增至6.0 bpp： BA从 98.12%降至 91.12%；FID约为 2.77~4.30	该结果表明扩散模型可支持较高载荷，但当载荷继续提高时，消息恢复准确率仍会下降
文献[22]方法	DM	ILSVRC 2013 ImageNet	1.0/3.0/6.0 bpp	高斯噪声下：逐比特错误率为5.46%/ 5.82%/ 5.24%，FID约为 3.21~3.28；图像压缩下：BER为6.48%/ 7.00%/ 7.15%，FI约为 4.69~4.81	在高斯噪声扰动下，CGIS仍保持相对稳定的逐比特错误率，说明其对噪声退化具有一定鲁棒性；在JPEG压缩质量因子(QF=50)条件下，CGIS 的误码率有所上升，但图像质量退化相对可控

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方法类别	基于GAN	基于FM	基于DM
主要载密位置	随机向量、生成器输入、隐层特征、解码器输出	可逆潜变量、频域编码、条件流潜空间	初始噪声、潜变量、DDIM轨迹、中间时间步、提示词/ 注意力
典型优势	推理快、结构直观、端到端训练	双向映射、恢复精确、分布可建模、速度较快	图像质量高、语义控制强、训练无关方案丰富
恢复机制	解码器恢复	正向流反演与信息解码	VAE编码、扩散反演与信息解码
模型假设	生成器可学习消息到图像映射	图像与潜变量之间近似可逆	潜空间/噪声空间可稳定反演
容量特点	可较高，但高载荷易产生伪影	高容量下需保持潜变量分布	容量灵活，但易影响噪声分布
图像质量	受训练稳定性影响	与FM表达能力相关	通常较高，语义可控性强
主要鲁棒性瓶颈	不可逆、训练不稳、高容量易伪影、OSN 后误码高	有损信道破坏严格可逆性，逆映射对压缩和缩放敏感	反演误差、采样开销、噪声分布偏移、提示词泄露、OSN 信道不稳定
代表方案	文献[2,3]方案	文献[4-6]方案	文献[10,16,18,19,22] 方案

数据类型	代表性数据集或来源	适用场景	主要评价作用
开放域自然图像与图文描述	文献[32,33] 方案	文生图隐写、开放域生成隐写、提示词驱动测试	评估语义一致性、生成多样性、开放场景下的秘密恢复能力
大规模图文对数据	文献[34,35] 方案	大规模提示词采样、开放域语义覆盖、社交媒体来源图像分析	扩展提示词和图像内容范围，检验方法在复杂开放域数据上的泛化能力
人脸域高质量图像	文献[36,37] 方案	人脸生成隐写、受限域生成、语义条件控制实验	评估高质量受限域图像中视觉质量、恢复稳定性和检测风险
场景与类别图像	文献[38,39,40]方案	场景条件生成、类别条件生成、受限域对比实验	评估特定类别或场景下的载密稳定性、图像质量和模型泛化能力
社交媒体风格图像	文献[41,42] 方案	OSN 有损传播测试、真实平台上传下载实验	评估压缩、缩放、格式转换、多轮重编码后鲁棒恢复能力
生成图像与提示词样本	文献[43,44]方案或 SD、GAN、Flow等黑盒文生图模型生成	生成式隐写、生成图像检测、黑盒生成图像场景	评估携密生成图像与普通生成图像之间的分布差异，以及噪声或潜空间检测风险

指标	类型	含义	评价作用
BER	鲁棒性	逐比特错误率	衡量基础恢复误差
BA/BRR	鲁棒性	比特级消息准确率/ 恢复率	直观反映平均恢复能力
EMR	鲁棒性	完整消息无误恢复比例	反映高容量通信可用性
PSR	鲁棒性	纠错和校验后的有效载荷成功比例	适用于含 ECC 的系统
名义容量	容量	理论可嵌入的信息量	衡量方法标称载荷
有效容量	容量	无失真下可正确恢复的容量	衡量实际可用载荷
鲁棒容量	容量	信道失真后仍可恢复的容量	衡量抗传播退化能力
安全容量	容量	检测风险可控下的容量	衡量安全可用载荷
鲁棒容量	容量	给定失真后可恢复容量	描述鲁棒条件下信道载荷的能力
AUC	安全性	安全检测ROC曲线下面积	衡量安全检测区分能力
Pe	安全性	安全检测出错概率	越接近 0.5 越安全
FPR	安全性	正常图被误判为载密图的比例	衡量误报风险
FNR	安全性	载密图被误判为正常图的比例	衡量逃避检测能力
EER	安全性	误报率与漏检率相等时的错误率	比较检测难度
F1-score	安全性	精确率与召回率的调和平均	综合衡量检测效果
MMD	安全性	两类特征分布差异	衡量潜空间偏移
Wasserstein 距离	安全性	两类分布整体距离	衡量噪声域偏移
PSNR	隐蔽性	像素保真度指标	图像重建质量
SSIM	隐蔽性	结构相似性指标	亮度、对比度和结构保持
LPIPS	隐蔽性	感知质量指标	深度特征层面的感知差异
FID	隐蔽性	生成质量/分布指标	生成图像与真实图像分布差异

安全检测层级	攻击者可见信息	典型检测工具	主要风险
图像域检测	最终图像	文献[46-50]工具	像素与纹理异常
生成图像检测	图像及生成痕迹	文献[51,52]工具	生成模型指纹
潜空间检测	潜变量或噪声表示	MMD、MLP、Transformer、文献[25] 工具	噪声分布偏移