Robust Image Steganography in Online Social Networks via Neural Style Transfer

February 11, 2026MathematicsOpen Access

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Key Points

The aim is to develop a robust framework for image steganography that overcomes limitations of existing schemes in online social networks.
Developed StegTransfer, a unified framework integrating various techniques.
Implemented forward non-differentiable distortion simulation to mimic OSN operations.
Utilized adversarial training with steganalyzers to enhance embedding security.
Applied style enhancement methods to preserve visual quality while maintaining embedding capacity.
Achieved a NIMA score of 6.32 for visual fidelity.
Demonstrated robustness with PSNR up to 30.2 dB under JPEG compression.
Secured detection rates as low as 15.5% under StegExpose.
Secured detection rates as low as 62.3% under SiaStegNet.

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This research demonstrates a novel framework for enhancing image steganography in social networks, suggesting improved visual quality and security.

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Luo et al. (2026) studied this question.

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