Fearless of Noise: Robust Image-in-Image Hiding Using Dual-Tree Complex Wavelet Transform and State Space Model

Image-in-image hiding, which embeds a full-size secret image into a cover image with minimal perceptual distortion and accurate recovery, has attracted increasing attention because of its wide applications in copyright protection, covert communication, and digital forensics. However, when facing full-size secret images, the secret data often exceed the capacity limitation of individual cover images, resulting in existing solutions struggling to achieve an effective balance among robustness, high load capacity, and resistance to steganalysis and detection capabilities, which is particularly prominent when encountering social noise interference in real-world scenarios. To address these challenges, we propose MambaRIS, a robust and efficient image steganography framework that combines the dual-tree complex wavelet transform (DTCWT) with a state space model. The DTCWT module enables directionally selective decomposition of the input, enriching frequency-domain representations and providing more resilient embedding regions for robust cross-frequency hiding and recovery. Furthermore, we introduce a Mamba-based autoencoder architecture equipped with a novel spatial channel Mamba block (SCMB), which integrates spatial and channel attention mechanisms with linear-time global dependency modeling, significantly improving embedding adaptability under complex noise and distortion conditions. Extensive experiments demonstrate the superiority of the proposed scheme in terms of visual quality, robustness, and resistance to steganalysis. In JPEG compression with a quality factor of Q = 80, our method achieves an average improvement in hiding and recovery accuracy (measured by PSNR) of 0.87 dB compared with state-of-the-art architectures, while reducing the number of parameters by 33%.