Abstract
Coverless image steganography has attracted considerable attention due to its ability to evade traditional steganalysis techniques. However, many existing coverless methods face difficulties in reliably recovering secret information when subjected to geometric attacks. To overcome this limitation, we propose a novel approach based on deep unsupervised hashing for coverless steganography, designed to improve resilience against geometric attacks. The method begins by developing a robust model for hash sequence generation. To further strengthen the modelβs resistance to a variety of attacks, we integrate an attention mechanism using depth-wise separable convolutions, combined with a Transformer module that captures global context and long-range dependencies. In this framework, the sender processes an image through the trained model to generate a hash sequence, while the receiver uses the same model to recover the original secret information from the stego image, ensuring the security of the steganographic process. Additionally, we employ an inverted index to facilitate efficient and rapid matching of stego images. Experimental evaluations show that the proposed method outperforms existing approaches in terms of robustness to both geometric and non-geometric attacks. Specifically, as the intensity of geometric attacks increases, our method consistently achieves high information recovery performance, with an average robustness score of 93.6% across several datasets. These results highlight the enhanced robustness and security of the proposed coverless steganographic approach.