CIIS-Net: A Key-Conditioned Camouflage-Integrated Invertible Network for Image Steganography

Image steganography must preserve image fidelity while limiting exposure of both hidden content and the hiding model. We present CIIS-Net, a key-conditioned camouflage-integrated invertible network that performs public image denoising without a key and secret-image hiding and recovery with the correct key. Its hidden-weight parameterization combines deterministic key-derived values with trainable residuals. Public pretraining is followed by joint optimization of public and secret parameters under a routine-preservation objective. Hiding and recovery share the forward and inverse directions of a Haar-wavelet affine coupling network. Across 250 cover–secret pairs from DIV2K, COCO, and ImageNet, the model achieves mean steganogram (stego) and secret-recovery peak signal-to-noise ratios (PSNRs) of 43.25 and 36.06 dB, respectively, under an eight-bit channel. Public denoising reaches 32.24 and 31.58 dB on 500 images at noise standard deviations of 15/255 and 20/255. Across three training runs, 1000 incorrect keys per run yield mean recovery PSNRs of 8.38–9.70 dB, demonstrating sensitivity to the evaluated key substitutions rather than formal security. On 1000 held-out cover/stego pairs, the evaluated StegExpose configuration yields an area under the receiver operating characteristic curve (AUC) of 0.5101, indicating near-chance discrimination. Yedroudj-Net yields an AUC of 0.9973 and 97.60% detection accuracy after training on 150 pairs for 150 epochs.