Low distortion and adaptive image steganography by enhancing DBSCAN, Sobel operator, and XOR coding

Abstract

Image steganography is concealing digital messages in an image in a matter that the secret data is not revealed. An effective practice to reduce the variations between the cover and stego images is to hide the secret data in the edge areas. The clustering approach can be applied to detect the edge pixels of the cover image. DBSCAN is a density-based clustering algorithm and is applicable for this purpose. This algorithm, however, may fail to detect edge areas if its parameters are not tuned properly. Furthermore, to reduce the distortion of the stego image, coding techniques should be applied in the data embedding process. The existing coding schemes have low performance and there is still scope for improvement and to propose more promising approaches. In this paper, we propose an effective edge-based image steganography algorithm. The proposed method convolves the cover image using an enhanced Sobel operator prior to edge detection. This approach places more emphasis on the edge pixels. For detecting edge areas, an adaptive clustering method based on the DBSCAN algorithm is proposed, which adjusts its sensitivity against edge pixels according to the cover image structure and message size. As opposed to the traditional DBSCAN, this method is adaptive and can adjust the values of parameters dynamically. Additionally, a novel enhanced XOR coding technique for message concealment which reduces the variations of the edge pixels to 12.5%, is proposed. The experimental results demonstrate that the proposed method improves PSNR and wPSNR by 3.98db and 2.18db compared to other state-of-the-art schemes.