Lightweight and robust image steganography method for secure communication.

Image steganography plays a crucial role in covert communication and copyright protection, but existing methods still struggle to achieve a balance between embedding capacity, image imperceptibility, anti-detection capabilities, and model complexity. Under complex channel interference such as compression and noise, the accuracy of secret information recovery easily declines, while computational costs increase. Therefore, this study proposes an image steganography method based on an end-to-end embedding-extraction network and lightweight convolutional modules. First, the carrier image and the secret image are input into the encoding network, where multi-scale spatial and channel features are extracted using improved Inception-V3 and Xception convolutional modules. Second, a RepVGG structure reparameterization mechanism is introduced to reduce computational complexity in the inference stage while maintaining feature representation capabilities. Finally, a dense connection mechanism is combined in the decoding network to enhance shallow feature reuse, and a discriminant network applies adversarial constraints to the carrier image and the steganographic image. Experimental results demonstrate that the proposed algorithm achieves a peak signal-to-noise ratio of 40.7 dB, a structural similarity index of 0.969, and a learned perceptual image patch similarity of 0.055 on the test set, all outperforming comparative methods. In terms of steganographic performance, its bit error rate is only 0.008, and the area under the curve is 0.398, also surpassing the other two comparative algorithms. Additionally, under strong compression conditions of 30, the proposed algorithm maintains a recovery accuracy of 0.82 and controls the bit error rate at 0.38, while under 90 compression, it achieves a recovery accuracy of 0.98 with the bit error rate dropping to 0.23. In noise interference experiments, the method retains a recovery accuracy of 0.96 and a bit error rate of 0.22 when the noise standard deviation is 0.05, demonstrating strong robustness. The findings indicate that this algorithm achieves a balance between performance and efficiency in steganographic capacity, image quality, and anti-interference capability, offering a more practical solution for image steganography applications.