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

DUAN Xintao; CHEN Rusheng; LI Sen; QIN Chuan

doi:10.11999/JEIT260857

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DUAN Xintao, CHEN Rusheng, LI Sen, QIN Chuan. Lightweight Image-to-image Steganography Based on Improved Bidimensional Empirical Mode Decomposition and a Dual-domain Graph Convolutional Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260857

Citation:

DUAN Xintao, CHEN Rusheng, LI Sen, QIN Chuan. Lightweight Image-to-image Steganography Based on Improved Bidimensional Empirical Mode Decomposition and a Dual-domain Graph Convolutional Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260857

Citation:

DUAN Xintao, CHEN Rusheng, LI Sen, QIN Chuan. Lightweight Image-to-image Steganography Based on Improved Bidimensional Empirical Mode Decomposition and a Dual-domain Graph Convolutional Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260857

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

doi: 10.11999/JEIT260857 cstr: 32379.14.JEIT260857

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

Received Date: 2026-06-25
Accepted Date: 2026-08-17
Rev Recd Date: 2026-08-15

Available Online: 2026-08-25

Abstract

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 49.65%, 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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References(34)

References

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