MAE-based image inpainting-steganography method

Abstract To address the current issue that existing steganographic techniques are highly dependent on the quality of carrier images and have insufficient adaptability to damaged images, we propose an MAE-based inpainting-steganography framework, which realizes steganography and inpainting for 2D color damaged images through multi-module collaboration. The encoder divides the original 244 × 244 images into non-overlapping patches and performs random masking to generate their feature sequences. The steganographic point prediction module locates suitable steganographic points by analyzing feature fluctuations during the inpainting process. The steganography module embeds information by introducing feature offsets at specific positions in the feature sequences according to the suitable steganographic points. The decoder completes the inpainting of masked regions to obtain stego-inpainted images. And the extraction module recovers the secret information through feature alignment. Experimental results show that with a feature fluctuation amplitude threshold of 0%, an offset of 3%, and an embedding capacity of 30 bits, the verification success rate reaches 96.67%, and remains at 95.27% even when the capacity is increased to 1000 bits. In terms of anti-steganography detection capability, evaluation results based on mainstream steganalysis models show that the detection accuracy of the proposed method is 49.79%. This accuracy is close to the level of random guessing, which indicates the excellent anti-detection performance of the method. Moreover, under the combined perturbations of JPEG compression, Gaussian blur, and noise interference, the information extraction accuracy still reaches 94.68%, demonstrating that the feature-domain embedding mechanism has good robustness. In addition, adding steganography under different masking ratios has no significant impact on image inpainting performance, and some metrics even outperform pure inpainting algorithms. While maintaining the quality of image inpainting, this framework achieves highly reliable information embedding and extraction, and maintains a high verification success rate in the complex scenario of damaged carrier images, providing a new idea for steganographic techniques in practical applications.