Lightweight image-to-image Steganography Based on Improved Emd and 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 Emd and Dual-domain Graph Convolutional Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260857

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DUAN Xintao, CHEN Rusheng, LI Sen, QIN Chuan. Lightweight image-to-image Steganography Based on Improved Emd and 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 Emd and 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: Key Scientific Research Project of Colleges and Universities in Henan Province (26A520016)

Received Date: 2026-06-24
Accepted Date: 2026-08-17
Rev Recd Date: 2026-08-17

Available Online: 2026-08-25

Abstract

Abstract

Objective Image steganography embeds secret information into a cover image to achieve secure transmission, and it serves as an important technique in confidential communication and privacy protection. With the development of deep learning, learning-based steganography has notably improved embedding capacity and reconstruction quality. However, three requirements, namely high steganographic performance, strong resistance to steganalysis, and lightweight design, are difficult to satisfy simultaneously, and this trade-off has become the main bottleneck for practical deployment. Single-domain processing methods cannot balance the three objectives, whereas existing high-performance models are structurally complex and hard to deploy in resource-constrained environments. Moreover, mainstream dual-domain schemes usually assume that the embedding distortion follows a continuous distribution in both the spatial domain and the frequency domain, yet actual steganographic modifications tend to concentrate in the discontinuous and complex texture regions of the cover image. To address these problems, a lightweight image steganography network is designed in this study to jointly optimize the embedding and extraction paths, so that the stego-image quality and the anti-steganalysis ability are improved while a low computational cost and a low inference latency are maintained. Methods A lightweight steganographic network named GISNet is proposed, in which an encoder-decoder architecture with skip connections is adopted and the hiding network and the extraction network are made structurally symmetric without weight sharing. First, an Improved Bidimensional Empirical Mode Decomposition (IBEMD) module is applied, by which the secret image is adaptively decomposed into several intrinsic mode components and one residual component, so that the hidden information is dispersed hierarchically among components of different morphology. Gaussian blur is used to replace extremum interpolation for estimating the local mean envelope, the separability of the Gaussian kernel is exploited to reduce the computational complexity, and a parameter-binding mechanism is introduced to guarantee deterministic and reversible reconstruction. Next, a multi-scale spatial-frequency block (IMFB) is designed, in which multi-branch dilated convolutions, an attention mechanism, dynamic gating, and a frequency-domain perception unit are integrated, so that the spatial features and the frequency features are deeply fused, the anti-detection ability is enhanced, and the high-fidelity extraction of the secret information is ensured. Finally, an improved graph fusion neural network (GFNN) is employed as the bottleneck layer, in which a sparse graph is constructed in the feature space through the K-nearest-neighbor algorithm and messages are propagated only among non-local nodes with high similarity, so that the long-range pixel associations are explicitly modeled, the modification patterns in discontinuous texture regions are characterized, and the model complexity is substantially reduced. The hiding network and the extraction network are jointly optimized by a four-term loss function that combines the hiding loss, the restriction loss, the Laplacian pyramid loss, and the perceptual loss. Results and Discussions GISNet achieves the best overall image-hiding and recovery performance on DIV2K, COCO, and ImageNet, demonstrating high reconstruction quality and stable cross-dataset generalization (表1). On DIV2K, the PSNR values reach 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 after 30-fold magnification (图5). Ablation experiments show that replacing DWT with IBEMD improves the PSNR values by 8.04 dB and 5.57 dB, respectively (表2). The complete combination of IBEMD, IMFB, and GFNN provides the best results, confirming the complementary effects of hierarchical information dispersion, multiscale spatial-frequency mapping, and nonlocal feature association (表3). Multi-dilation-rate branches and joint spatial-frequency processing further improve hiding quality and recovery accuracy (表4,表5). The detection accuracies under four steganalysis methods range from 49.35% to 49.85%, indicating that the stego images are difficult to distinguish from natural cover images (表6). GISNet requires only 8.00 M parameters and 7.88 GFLOPs, with an inference time of 76 ms (表7). These results demonstrate that GISNet effectively balances visual quality, recovery accuracy, resistance to steganalysis, and computational efficiency. Conclusions A lightweight dual-domain graph convolutional network for image steganography, named GISNet, is proposed in this paper. The experimental results demonstrate the following. (1) The improved bidimensional empirical mode decomposition disperses the secret information hierarchically and supports deterministic and reversible reconstruction, by which a reliable basis is provided for high-quality hiding and recovery. (2) The multi-scale spatial-frequency block and the graph fusion neural network jointly improve the stego-image quality and the anti-steganalysis ability, so that the stego images can resist detection by multiple steganalysis tools. (3) The lightweight design, which is based on depth-wise separable convolutions and sparse graph construction, significantly reduces the model complexity and the computational cost, by which the model is made suitable for deployment in resource-constrained environments. Future work will focus on the robustness against channel interference and the extension to multi-image and cross-modal steganography, so that the security and applicability of the scheme are further enhanced.
- Image steganography,
- Empirical mode decomposition,
- Dual-domain model,
- Graph convolutional network

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References(34)

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