基于改进经验模态分解与双域图卷积的轻量化图像隐写网络

段新涛; 陈儒生; 李森; 秦川

doi:10.11999/JEIT260857

基于改进经验模态分解与双域图卷积的轻量化图像隐写网络

doi: 10.11999/JEIT260857 cstr: 32379.14.JEIT260857

段新涛^{1, 2, ,},
陈儒生¹,
李森¹,
秦川³

1.
河南师范大学计算机与信息工程学院新乡 453007
2.
河南省教育人工智能与个性化学习重点实验室新乡 453007
3.
上海理工大学光电与计算机工程学院上海 200093

基金项目: 河南省高等学校重点科研项目(26A520016)

详细信息

作者简介:
段新涛：男，副教授，博士，研究方向为信息隐藏，AI安全、深度学习

陈儒生：男，硕士生，研究方向为图像隐写术

李森：男，硕士，研究方向为图像隐写术

秦川：男，教授，博士，研究方向为多媒体智能计算、AI安全、深度学习、数字图像处理、信息隐藏、密文域信号处理、数字取证

通讯作者:
段新涛　duanxintao@htu.edu.cn

中图分类号: TN957.52
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- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2026-06-25
- 修回日期: 2026-08-15
- 录用日期: 2026-08-17
- 网络出版日期: 2026-08-25

Lightweight Image-to-image Steganography Based on Improved Bidimensional Empirical Mode Decomposition and a Dual-domain Graph Convolutional Network

DUAN Xintao^{1, 2
, ,},
CHEN Rusheng¹,
LI Sen¹,
QIN Chuan³

1.
Henan Normal University, Xinxiang 453007, China
2.
Key Laboratory of Educational Artificial Intelligence and Personalized Learning of Henan Province, Xinxiang 453007, China
3.
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Funds: Key Scientific Research Project of Colleges and Universities in Henan Province (26A520016)

摘要

摘要: 针对现有单域图像隐写方法面临的难以同时兼顾隐蔽性、抗隐写分析能力与运行效率，以及高性能模型结构复杂、难以轻量化部署等问题，该文提出一种基于改进经验模态分解与双域图卷积的轻量化图像隐写网络模型。首先，利用改进的二维经验模态分解模块将秘密图像自适应分解为多尺度分量，以实现隐秘信息在不同形态组件中的自适应层级分散；其次，设计多尺度空频块，通过融合多分支扩张卷积、注意力机制与频域感知单元，实现空间域与频域特征的深度融合与协同优化，在增强抗检测能力的同时确保秘密信息的高保真提取；最后，引入改进的图特征神经网络作为瓶颈层，利用高效的稀疏图卷积操作显式建模远距离像素关联，在有效表征非连续纹理区修改模式的同时大幅降低模型计算复杂度。实验结果表明，该模型参数量仅为8.00 M，计算成本仅为7.88 GFLOPs，单幅图像推理时延低至76 ms，展现出显著的轻量化特征与高运行效率，更适用于资源受限的实际部署环境。
- 图像隐写 /
- 经验模态分解 /
- 双域模型 /
- 图卷积网络
Abstract: Objective Image steganography embeds secret information into a cover image for secure transmission and is an important technique for confidential communication and privacy protection. With the development of deep learning, learning-based steganography has substantially improved embedding capacity and reconstruction quality. However, high steganographic performance, strong resistance to steganalysis, and lightweight design remain difficult to achieve simultaneously. This trade-off has become a major obstacle to practical deployment. Single-domain methods have difficulty balancing these requirements, whereas existing high-performance models are often structurally complex and difficult to deploy in resource-constrained environments. Moreover, mainstream dual-domain schemes generally assume that embedding distortion follows a continuous distribution in the spatial and frequency domains. In practice, however, steganographic modifications tend to concentrate in discontinuous, complex-texture regions of the cover image. To address these problems, a lightweight image steganography network is designed to jointly optimize the embedding and extraction paths. The aim is to improve stego image quality and resistance to steganalysis while maintaining low computational cost and inference latency. Methods A lightweight steganographic network named GISNet is proposed. An encoder-decoder architecture with skip connections is adopted, and the hiding and extraction networks are structurally symmetric but do not share weights. First, Improved Bidimensional Empirical Mode Decomposition (IBEMD) is used to adaptively decompose the secret image into multiple intrinsic mode functions and a residual component. The secret information is thereby hierarchically dispersed among components with different morphological characteristics. Gaussian blur replaces extremum interpolation to estimate the local mean envelope, and the separability of the Gaussian kernel is used to reduce computational complexity. Fixed Gaussian parameters and ensemble averaging are further used to support deterministic and reversible reconstruction. Next, the Innovative Multispatial FreqBlock (IMFB) is designed by integrating multibranch dilated convolutions, an attention mechanism, dynamic gating, and frequency-domain processing. Spatial- and frequency-domain features are jointly processed to improve resistance to detection while maintaining high-fidelity extraction of secret information. Finally, the GraphFusion Neural Network (GFNN) is used as the bottleneck layer. A sparse graph is constructed in the feature space using the K-nearest-neighbor algorithm, and messages are propagated only among highly similar nonlocal nodes. Long-range pixel associations are therefore modeled explicitly, modification patterns in discontinuous texture regions are represented more effectively, and model complexity is substantially reduced. The hiding and extraction networks are jointly optimized using a four-term loss function consisting of the hiding loss, restriction loss, Laplacian pyramid loss, and perceptual loss. The terminology and module functions follow those defined in the main manuscript. Results and Discussions GISNet achieves the best overall hiding and recovery performance on DIV2K, COCO, and ImageNet, demonstrating high reconstruction quality and strong cross-dataset generalization (Table 1). On DIV2K, the Peak Signal-to-Noise Ratio (PSNR) reaches 58.07 dB for cover/stego image pairs and 60.10 dB for secret/recovered secret image pairs. The cover and stego images are visually indistinguishable, and the residual maps remain nearly black even after 30-fold magnification (Fig. 5). Ablation experiments show that replacing the Discrete Wavelet Transform (DWT) with IBEMD increases the PSNR by 8.04 dB and 5.57 dB for the two types of image pairs, respectively (Table 2). The complete combination of IBEMD, IMFB, and GFNN produces the best results. These findings confirm the complementary effects of hierarchical information dispersion, multiscale spatial-frequency mapping, and nonlocal feature association (Table 3). Multi-dilation-rate branches and joint spatial-frequency processing further improve hiding quality and recovery accuracy (Tables 4 and 5). Under four steganalysis methods, the detection accuracies range from 49.35% to 49.85%, indicating that the stego images are difficult to distinguish from natural cover images (Table 6). GISNet contains only 8.00 M parameters and requires 7.88 GFLOPs, with an inference time of 76 ms (Table 7). These results demonstrate that GISNet effectively balances visual quality, recovery accuracy, resistance to steganalysis, and computational efficiency. The manuscript reports the same comparative and computational results. Conclusions A lightweight dual-domain graph convolutional network for image steganography, named GISNet, is proposed. The experimental results support the following conclusions. (1) IBEMD hierarchically disperses secret information and supports deterministic and reversible reconstruction, providing a reliable basis for high-quality hiding and recovery. (2) IMFB and GFNN jointly improve stego image quality and resistance to steganalysis, enabling the stego images to resist detection by multiple steganalysis tools. (3) The lightweight design based on depthwise separable convolutions and sparse graph construction significantly reduces model complexity and computational cost, making GISNet suitable for deployment in resource-constrained environments. Future work will focus on robustness against channel interference and extensions to multi-image and cross-modal steganography to further improve the security and applicability of the proposed scheme. The lightweight mechanisms are consistent with the manuscript, which attributes the low computational cost to depthwise separable convolutions in IMFB and KNN-based sparse graph construction in GFNN.
- Image steganography /
- Empirical mode decomposition /
- Dual-domain model /
- Graph convolutional network

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图 1 在计算复杂性和推理时间方面对GISNet与最新前沿(SOTA)方法的比较分析

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图 2 所提GISNet框架概述

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图 3 IMFB的结构

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图 4 GFNN的结构

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图 5 可视化图像隐藏效果

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表 1 单图像隐藏结果

方法	载体/载密图像对
	DIV2K (1024 × 1024)				COCO (256 × 256)				Imagenet (256 × 256)
	PSNR	SSIM	MAE	RMSE	PSNR	SSIM	MAE	RMSE	PSNR	SSIM	MAE	RMSE
HiDDeN^[7]	32.48	0.9172	9.73	13.93	32.98	0.9137	9.66	13.39	32.95	0.9124	9.87	13.60
UDH^[31]	44.68	0.8913	3.58	4.43	43.89	0.8988	3.79	4.65	43.87	0.9018	3.81	4.66
HiNet^[3]	50.79	0.9926	1.46	2.06	45.98	0.9806	2.43	3.56	46.00	0.9853	2.49	3.55
DeepMIH^[10]	43.73	0.9873	2.21	3.14	43.13	0.9721	2.83	3.91	43.29	0.9621	2.74	4.21
DAH-Net^[29]	49.39	0.9896	1.72	2.79	46.15	0.9856	2.96	3.84	45.75	0.9857	2.98	3.78
StegFormer^[30]	56.30	0.9956	0.73	1.23	48.77	0.9884	1.41	2.48	48.79	0.9859	1.51	2.50
CamStegNet^[32]	38.36	0.9798	/	3.26	40.05	0.9776	/	2.70	39.49	0.9774	/	2.88
本文	58.07	0.9972	0.64	1.07	49.42	0.9911	1.03	2.35	48.96	0.9894	1.43	2.41

方法					秘密/恢复秘密图像对
	DIV2K (1024 × 1024)				COCO (256 × 256)				Imagenet (256 × 256)
	PSNR	SSIM	MAE	RMSE	PSNR	SSIM	MAE	RMSE	PSNR	SSIM	MAE	RMSE
HiDDeN^[7]	39.24	0.9502	3.54	5.29	36.29	0.9235	5.32	8.01	36.02	0.9132	5.02	7.32
UDH^[31]	42.02	0.9781	2.15	3.23	34.75	0.9175	4.82	7.79	34.62	0.9034	5.29	8.22
HiNet^[3]	52.32	0.9936	0.88	1.28	47.06	0.9796	1.82	2.78	47.07	0.9764	1.95	2.86
DeepMIH^[10]	47.92	0.9892	1.76	2.54	45.31	0.9698	2.83	3.53	45.83	0.9889	2.79	3.54
DAH-Net^[29]	50.72	0.9896	1.54	1.98	47.46	0.9726	1.54	2.17	47.35	0.9834	2.07	2.95
StegFormer^[30]	55.45	0.9964	0.73	1.08	49.21	0.9882	1.48	2.33	49.18	0.9851	1.62	2.41
CamStegNet^[32]	31.94	0.9314	/	6.70	32.47	0.9273	/	6.30	31.95	0.9192	/	6.96
本文	60.10	0.9964	0.72	0.97	53.43	0.9929	1.11	2.04	50.10	0.9907	1.51	2.17
注：“/”表示该对应指标未在原始出版物中报告，且该方法的源代码尚未公开发布；最佳结果以粗体显示，次优结果以下划线标出。

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表 2 不同分解方法的实验结果

分解方式	载体/载密图像对				秘密/恢复秘密图像对
分解方式	PSNR(dB)	SSIM	MAE	RMSE	PSNR(dB)	SSIM	MAE	RMSE
DWT	50.03	0.9846	1.64	2.77	54.53	0.9914	0.96	0.97
IBEMD	58.07	0.9972	0.64	1.07	60.10	0.9964	0.72	0.97

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表 3 IBEMD, IMFB和GFNN的消融实验结果

IBEMD	IMFB	GFNN	载体/载密图像对				秘密/恢复秘密图像对
IBEMD	IMFB	GFNN	PSNR(dB)	SSIM	MAE	RMSE	PSNR(dB)	SSIM	MAE	RMSE
×	×	×	30.83	0.9803	4.08	9.89	38.33	0.9814	2.29	2.19
×	√	×	53.24	0.9930	0.90	1.55	52.17	0.9927	0.96	1.67
×	×	√	51.50	0.9926	0.94	1.78	52.91	0.9931	0.99	1.88
×	√	√	54.31	0.9938	1.13	1.53	53.31	0.9949	0.84	1.20
√	×	×	52.46	0.9914	0.91	1.48	51.28	0.9937	0.91	1.80
√	√	×	54.19	0.9937	0.81	1.23	56.78	0.9956	0.89	1.20
√	×	√	54.27	0.9942	0.79	1.37	56.34	0.9951	0.90	1.31
√	√	√	58.07	0.9972	0.64	1.07	60.10	0.9964	0.72	0.97

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表 4 膨胀卷积的消融实验结果

扩展分支	载体/载密图像对				秘密/恢复秘密图像对
扩展分支	PSNR(dB)	SSIM	MAE	RMSE	PSNR(dB)	SSIM	MAE	RMSE
×	52.96	0.9943	0.97	1.57	54.98	0.9950	0.90	1.57
√	58.07	0.9972	0.64	1.07	60.10	0.9964	0.72	0.97

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表 5 不同域的使用的实验结果

空域	频域	载体/载密图像对				秘密/恢复秘密图像对
空域	频域	PSNR(dB)	SSIM	MAE	RMSE	PSNR(dB)	SSIM	MAE	RMSE
×	√	51.78	0.9927	0.99	1.54	53.36	0.9929	1.12	2.01
√	×	52.43	0.9951	0.86	1.44	56.16	0.9950	0.88	1.53
√	√	58.07	0.9972	0.64	1.07	60.10	0.9964	0.72	0.97

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表 6 基于XuNet, ZhuNet, SiaStegNet和StegExpose的8种隐写方案的检测准确率(%)

方法	XuNet	ZhuNet	SiaStegNet	StegExpose
Baluja^[2]	72.21	87.32	77.34	/
UDH^[31]	75.61	78.37	76.38	/
StegoPNet^[33]	70.76	71.93	68.95	/
HiNet^[3]	59.64	56.45	53.45	/
StegFormer^[30]	58.96	55.54	55.49	/
Ma^[34]	51.80	59.70	/	/
CamStegNet^[32]	/	/	/	55.00
本文	49.35	49.85	49.80	49.65
注：精度越接近50%，性能越好。

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表 7 各方法在参数数量、计算量和每样本推理时间上的比较

方法	参数量(M)	运算量(G)	推理时间(ms)
UDH^[31]	16.66	16.30	127
HiNet^[3]	4.05	66.25	252
DeepMIH^[10]	5.40	88.33	318
StegFormer^[30]	17.46	9.63	131
本文	8.00	7.88	76

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