Secret image sharing (SIS) schemes have undergone significant development. However, to the best of our knowledge, none of the existing schemes has considered the deniable property during secret sharing. This presents a problem when we need to share secret images through an untrusted and supervised channel, where we may be coerced to reveal the secret to adversary. Here we propose a deniable SIS (DSIS) scheme. Before sharing the secret image, we manipulate the secret area of the image to create a forged image that possesses deniability. Then we employ SIS to distribute the forged image, while generating an auxiliary matrix derived from secret key. This matrix governs rules for sharing secret area. In the event of coercion to reveal the secret, we have the capability to present the adversary with the forged image instead, thereby retaining control over the disclosure of the secret area at our discretion. In DSIS, we can obtain a secret image with the small-sized secret key losslessly, while we can recover another visually-meaningful fake image to safeguard the secrecy and protect ourselves when facing coercion.

Secret image sharing (SIS) has made significant progress in research and has found wide applications. However, we note that shadows of traditional SIS contain a large amount of redundancy. A novel Shadow-Embeddable Secret Image Sharing scheme (SE-SIS) leveraging the redundancy in the shadows is proposed in this paper. SE-SIS utilizes the random values in Lagrange polynomials of traditional secret image sharing (SIS) scheme, and modifies a shadow to embed another secret image with a secret key. Then other shadows are modified simultaneously according to the properties of Lagrange polynomials to ensure the accurate recovery of the previously shared image. It is worth noting that embedding process does not impact the recovery of the shared image, and the embedded shadow is indistinguishable from the others. SE-SIS modifies noise-liked shadows into other noise-liked ones without affecting the recovery process, thereby achieving a high embedding rate. Meanwhile, SE-SIS realizes lossless recovery for both the shared image and the secret image. Experimental results indicate SE-SIS constructs randomized shadows and exhibits excellent performance in terms of Peak Signal to Noise Ratio (PSNR) and embedding rate.

Image steganography aims to conceal secret messages within an image without detection and has a long development history. With the rapid development of generative artificial intelligence, generative image steganography is also thriving. However, existing generative steganography methods struggle to balance steganographic capacity, extraction accuracy, and security. This paper proposes a high-capacity generative steganography method based on the first-order DPM-Solver, called StegaFDS. By utilizing our efficiently designed mapping function, which connects secret messages to the noise space of the diffusion probabilistic model (DPM), we achieve a significant increase in hiding capacity and detection resistance, while maintaining distribution-preserving. To improve message extraction accuracy further, we also optimize the existing first-order DPM-Solver inversion. Additionally, based on pre-trained diffusion models, StegaFDS can generate high-quality stego images without training. Experimental results show that StegaFDS performs exceptionally better than other generative steganography methods in the abovementioned metrics and demonstrates strong potential and availability.