Abstract: Image steganography embeds secret data into cover images for covert communication and is an important topic in information and multimedia security. With social-media compression, format conversion, image resampling, and active steganalysis, traditional methods face more complex scenarios. Existing deep steganography methods mainly focus on visual imperceptibility and embedding capacity, but pay insufficient attention to message confidentiality, integrity authentication, and error tolerance after extraction. Thus, covert transmission, content protection, and robust recovery are still difficult to unify. To address these problems, this paper proposes an information-encryption-driven high-security image steganography model. It jointly designs authenticated encryption, error-correction coding, key-controlled scrambling, and a deep steganographic network to achieve secure, covert, and reliable transmission over complex channels. At the payload generation stage, an “encryption-error correction-scrambling” defense scheme is built. HKDF-SHA256 is used to derive encryption and scrambling keys. AES-GCM provides authenticated encryption and generates ciphertext with confidentiality and integrity verification. Reed-Solomon coding is introduced to provide symbol-level error correction for the steganographic channel. If the number of erroneous symbols after inverse scrambling is within the RS correction radius, the correct data packet can be recovered. If the error exceeds the correction ability or AES-GCM authentication fails, decryption is stopped to avoid incorrect plaintext output. In addition, CSPRNG-based position and bit scrambling reduce payload correlation and statistical bias, while a sparse bitmap controls embedding positions and reduces structural clues exploitable by steganalyzers. At the embedding stage, a hybrid U-Net combining MS-DiSpAC and ViT is designed. MS-DiSpAC extracts texture, edge, and local structural features through multi-scale convolution, and uses dilated spatial attention to enlarge the receptive field while preserving resolution. It guides high-entropy payloads into complex texture regions. ViT supplements global context modeling and improves long-range dependency representation. The network generates stego images through a residual perturbation map and an intensity map, balancing image fidelity and recovery stability under high payloads. A WGAN discriminator with Wasserstein distance is further used for adversarial distribution alignment, making stego images statistically closer to cover images and reducing detection by SRNet, ZhuNet, and other steganalyzers. Experiments are conducted on ImageNet, COCO, and Visual Genome, including performance, generalization, payload whitening, robustness, and ablation tests. Metrics include PSNR, MS-SSIM, LPIPS, BER, ESR, ACC.1, ACC.2, and Dacc. At 0.4 bpp, the proposed method achieves 38.65 dB PSNR, 0.975 MS-SSIM, 0.036 LPIPS, and 99.14% bit recovery accuracy on ImageNet. Payload whitening results show that, after AES-GCM encryption, RS coding, and dual scrambling, single-bit entropy increases from 0.8932 to 0.9998, and average absolute autocorrelation decreases from 0.1285 to 0.0028. The final payload is close to random. Compared with representative methods, the proposed model achieves a better balance among visual fidelity, information recovery, and anti-steganalysis capability. It also maintains high recovery success under complex distortions within the RS correction range, providing a feasible solution for high-security image steganography in real network environments.

摘要： 图像隐写通过将秘密信息嵌入普通载体图像实现隐蔽通信，是信息安全与多媒体安全领域的重要研究方向。随着社交媒体平台压缩、格式转换、图像重采样以及主动隐写分析技术的发展，传统图像隐写方法面临更加复杂的应用环境。同时，现有深度隐写方法多集中于提升视觉不可感知性和嵌入容量，对提取后消息的内容机密性、完整性认证、误码容忍能力关注不足，导致隐蔽传输、内容保护与鲁棒恢复之间仍难以形成统一机制。针对上述问题，本文提出一种信息加密驱动的高安全性图像隐写模型，将认证加密、纠错编码、密钥控制置乱与深度隐写网络协同设计，以实现复杂信道下秘密消息的安全、隐蔽和可靠传输。载荷生成层面，构建“加密-纠错-置乱”主动防御体系：利用HKDF-SHA256派生加密密钥和置乱密钥，采用AES-GCM认证加密，生成兼具机密性与完整性校验能力的密文载荷；同时引入Reed-Solomon纠错编码，为隐写信道提供符号级误码修复能力。当反置乱后的码字符号错误数不超过RS纠错半径时，系统能够恢复正确数据包；若错误超出纠错能力或认证失败，则终止解密，避免错误明文输出。进一步采用CSPRNG驱动的位置置乱与比特置乱策略，打散载荷空间相关性和统计偏置，并生成稀疏位图控制嵌入位置，降低可被隐写分析器利用的结构线索。隐写嵌入层面，设计融合MS-DiSpAC与ViT的混合U-Net架构。MS-DiSpAC通过多尺度卷积提取纹理、边缘和局部结构信息，并利用膨胀空间注意力在保持分辨率的同时扩大感受野，引导高熵载荷嵌入复杂纹理区域；ViT补充全局上下文建模能力，弥补卷积结构对长距离依赖表达不足。网络输出残差扰动图和扰动强度图，通过加权残差调制生成隐写图像，在较高有效载荷下兼顾图像保真度与恢复稳定性。最后，引入WGAN判别器并采用Wasserstein距离进行对抗分布对齐，使隐写图像统计分布贴近载体图像，降低嵌入痕迹被SRNet、ZhuNet等隐写分析器捕获的概率。为验证方法有效性，本文在ImageNet、COCO和Visual Genome数据集上开展性能、泛化、载荷白化、鲁棒性及消融实验，并采用PSNR、MS-SSIM、LPIPS、BER、ESR、ACC.1、ACC.2和Dacc等指标评价。实验结果表明，在0.4bpp有效载荷条件下，本文方法在ImageNet上取得38.65dB的PSNR、0.975的MS-SSIM和0.036的LPIPS，提取端原始比特恢复准确率达到99.14%。载荷白化实验显示，原始消息经AES-GCM加密、RS编码和双重置乱后，单比特熵由0.8932提升至0.9998，平均绝对自相关由0.1285降至0.0028，最终载荷接近随机序列。与代表性方法相比，本文模型在视觉保真、信息恢复和抗隐写分析能力之间取得更优平衡；复杂失真条件下仍能在RS纠错能力范围内保持较高恢复成功率，为真实网络环境下的高安全图像隐写提供了可行方案。