Style as Cover: Deep Image Steganography via Stylized Transmission

Qi Li; Jidong Yang; Huaike Yu; Chunpeng Wang; Suo Gao; Herbert Ho-Ching Iu; Yuantian Miao; Bin Ma

cs.CVSep 18, 2026

Style as Cover: Deep Image Steganography via Stylized Transmission

Authors: Qi Li, Jidong Yang, Huaike Yu, Chunpeng Wang, Suo Gao, Herbert Ho-Ching Iu, Yuantian Miao, Bin Ma,

Organizations: Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, and Shandong Provincial Key Laboratory of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China · School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China · School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, WA 6009, Australia · School of Computer and Information Sciences, The University of Newcastle, College of Engineering, Science and Environment, Callaghan, NSW 2308, Australia

Abstract

Image steganography hides secret message within normal images, with most existing works relying on cover-preserving transmission. However, such a paradigm becomes vulnerable once the original cover is exposed or can be reliably approximated. In this paper, we propose StyleStegaNet, a stylized image hiding framework that replaces cover matching with style-concealment transmission. Instead of transmitting a cover-like stego image, StyleStegaNet generates stylized stego images conditioned on publicly available style references, redefining steganography invisibility from cover-preserving concealment to behavior-level camouflage based on style transformation. Such a setting poses a substantial challenge to reliable secret recovery, since neural stylization can significantly alter the feature statistics exploited by deep hiding methods. To address this challenge, StyleStegaNet decouples the overall task into four coordinated stages: stego generation, stylized transmission, structure-preserving reconstruction, and secret recovery. Moreover, StyleStegaNet is optimized with a progressive three-stage training strategy, in which wavelet-domain constraints and perceptual supervision guide the recoverable information toward structural representations. We further provide an analysis showing that secret recoverability is largely restricted to the normalized structural subspace, offering a mechanistic explanation for why directly stylized baselines fail and why a reconstruction-guided recovery path is necessary. Extensive experiments on DIV2K and MS-COCO datasets demonstrate the effectiveness of StyleStegaNet. And few-shot image steganalysis with two deep detectors further shows detection accuracy near random guessing, approximately 51%.

Figures & tables

Figure 1: Comparison with conventional image hiding. StyleStegaNet transmits a recoverable stylized stego image instead of a stego image close to the cover.

Notation	Description
$\mathbf{S}$	Secret image
$\mathbf{C}$	Cover image
$\mathbf{Y}$	Style image
$\mathbf{X}$	Stego image
$\mathbf{T}$	Stylized stego image transmitted by the sender
$\mathbf{X}^{\prime}$	Recovered stego image

Table 1: Summary of Notation.

Figure 3: StyleStego generation at the sender: a learnable steganographic encoder $E_{\theta}$ followed by a frozen stylization network $F_{\phi}$ .

Figure 4: Architecture of the RevStego network $R_{\psi}$ and secret decoder $D_{\eta}$ .

Stage	Objective	Evaluated pair	PSNR(dB)
Stage 1	$\mathcal{L}_{\mathrm{S1}}$	$(\mathbf{X},\mathbf{C})$	38.63
Stage 2	$\mathcal{L}_{\mathrm{S2}}$	$(\mathbf{X}^{\prime},\mathbf{X})$	32.78
Stage 3	$\mathcal{L}_{\mathrm{S3}}$	$(\mathbf{S}^{\prime},\mathbf{S})$	28.49

Table 2: DIV2K training-stage completion PSNR of StyleStegaNet.

Stylized transmission: Secret/Recovered image pair $(\mathbf{S}^{\prime},\mathbf{S})$
Methods	DIV2K				MS-COCO
	PSNR(dB) $\uparrow$	SSIM $\uparrow$	APD $\downarrow$	RMSE $\downarrow$	PSNR(dB) $\uparrow$	SSIM $\uparrow$	APD $\downarrow$	RMSE $\downarrow$
Deep Steganography [ 1 ]	7.71	0.1106	90.37	108.36	8.07	0.1387	85.65	103.64
HiDDeN [ 9 ]	9.76	0.1014	70.46	85.97	10.18	0.1192	67.52	82.28
HiNet [ 3 ]	7.94	0.0443	85.24	103.49	8.07	0.0415	83.83	101.94
LSB	7.83	0.0116	85.30	104.04	7.83	0.0128	85.50	104.07

Table 3: Stylized-transmission stress test on

(\mathbf{S}^{\prime},\mathbf{S})

. Conventional baselines use stego

\rightarrow

stylization

\rightarrow

original decoder.

Figure 5: Qualitative comparison under stylized transmission. Conventional reveal paths degrade after stylization, while StyleStegaNet remains recoverable.

Method	SiaStegNet		SRNet
	Avg.	$N{=}100$	Avg.	$N{=}100$
StyleStegaNet	51.00	50.75	51.50	51.50
PUSNet	54.65	57.75	62.23	70.50
BalujaNet	65.38	74.50	77.17	81.75
HiDDeN	74.12	93.25	61.83	62.50
HiNet	73.03	94.50	76.42	93.00

Table 4: Deep steganalysis accuracy (%) of the SiaStegNet [ 41 ] and SRNet [ 8 ] detectors. Lower is better;

50\%

is random guessing.

Figure 6: Few-shot steganalysis accuracy with the SiaStegNet [ 41 ] and SRNet [ 8 ] detectors. The dashed line marks random guessing; lower accuracy indicates lower detectability.

Figure 7: Magnified residual maps under stylized transmission ( $10{\times}$ ). In this cover-known diagnostic, StyleStegaNet residuals do not visually expose the secret structure; this is not a guarantee against adaptive attacks.

DIV2K: $(\mathbf{S}^{\prime},\mathbf{S})$
Methods	PSNR $\uparrow$	SSIM $\uparrow$	APD $\downarrow$	RMSE $\downarrow$
Full StyleStegaNet	27.02	0.8830	7.71	12.07
Context refiner	24.91	0.8144	9.90	15.35
w/o secret refinement	23.83	0.7663	11.42	17.29
w/o high-frequency loss	24.86	0.8065	9.96	15.44
Late secret refinement	24.72	0.8056	10.14	15.69

Table 5: Stage 3 ablation on

(\mathbf{S}^{\prime},\mathbf{S})

. Arrows indicate the preferred direction.

Zero-Shot Interpretable Image Steganalysis for Invertible Image Hiding
May 2, 2026Hao Wang, Yiming Yao, Yaguang Xie +2Digital Image Forensics
Zhejiang Provincial Key Laboratory for Sensitive Data Security Protection and Confidentiality Management, Hangzhou Dianzi University, Hangzhou 310018, China · Arcvideo Technology, Hangzhou 310018, China
StegGNN: Learning Graphical Representation for Image Steganography
Sep 26, 2026Abhinav Kumar, Shorya Singhal, Agam Pandey +2Graph Neural Networks
Indian Institute of Technology Roorkee
$\mathbf{S^2LM}$ : Towards Semantic Steganography via Large Language Models
Nov 7, 2025Huanqi Wu, Huangbiao Xu, Yongliang Xu +3Language Model Steganography
College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China

May 2, 2026J/K move · Enter open · S save

Zero-Shot Interpretable Image Steganalysis for Invertible Image Hiding

Hao Wang, Yiming Yao, Yaguang Xie, Tong Qiao+1

Zhejiang Provincial Key Laboratory for Sensitive Data Security Protection and Confidentiality Management, Hangzhou Dianzi University, Hangzhou 310018, China · Arcvideo Technology, Hangzhou 310018, China

Digital Image Forensics

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Image steganalysis, which aims at detecting secret information concealed within images, has become a critical countermeasure for assessing the security of steganography methods, especially the emerging invertible image hiding approaches. However, prior studies merely classify input images into two categories (i.e., stego or cover) and typically conduct steganalysis under the constraint that training and testing data must follow similar distribution, thereby hindering their application in real-world scenarios. To overcome these shortcomings, we propose a novel interpretable image steganalysis framework tailored for invertible image hiding schemes under a challenging zero-shot setting. Specifically, we integrate image hiding, revealing, and steganalysis into a unified framework, endowing the steganalysis component with the capability to recover the secret information embedded in stego images. Additionally, we elaborate a simple yet effective residual augmentation strategy for generating stego images to further enhance the generalizability of the steganalyzer in cross-dataset and cross-architecture scenarios. Extensive experiments on benchmark datasets demonstrate that our proposed approach significantly outperforms the existing steganalysis techniques for invertible image hiding schemes.

Abstract

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