Image steganography based on Kirsch edge detection
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Image steganography based on Kirsch edge detection Sudipta Kumar Ghosal1 · Agneet Chatterjee2 · Ram Sarkar2 Received: 10 August 2020 / Accepted: 29 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Conventional steganography methods fabricate the secret information into the cover pixels without analyzing the pixel intensities of an image. As a result, some minor pixel level manipulations may lead to huge visual distortion in the stego-image. To this end, in this paper, a novel steganographic scheme based on Kirsch edge detector is proposed. The aim of the scheme is to maximize the payload by embedding more secret bits into edge pixels and fewer bits into the non-edge pixels. The proposed scheme has three major phases: construction of edge image, embedding and extraction. The first phase deals with the construction of masked image from the cover image, and in turn, edge image from the masked one. The second phase deals with the decomposition of the cover image into a set of triplet of pixels and then embedding of (x + y + 1) bits of secret data into each triplet of pixels to obtain the stego-image. Here, ‘x’ and ‘y’ are not fixed as the edge information of each triplet changes incessantly. The third or last phase deals with the extraction of the secret information from the stego-image using the reverse process. Simulation results on some standard images ensure that the proposed method achieves higher payload and better image quality compared to the conventional steganographic schemes. Furthermore, the Kirsch edge detector is able to produce more number of edge pixels compared to the traditional edge detectors; and, hence the proposed scheme also outperforms the existing edge-based methods in terms of payload. Keywords Steganography · Edge detector · Kirsch · Payload · Image quality
1 Introduction With the rapid growth of image contents, the challenge of maintaining privacy of such contents over the internet has become a major concern in the recent times. The intruders are always try to access the useful image content using efficient image retrieval algorithms [1] and then launch a surprise attack in the form of image malware. Using these images and deep learning models, machines can accurately
Communicated by Y. Zhang. * Sudipta Kumar Ghosal [email protected] Agneet Chatterjee [email protected] Ram Sarkar [email protected] 1
Department of Computer Science and Technology, Nalhati Government Polytechnic, Birbhum, Nalhati 731243, India
Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
2
identify and classify objects and then explore several applications in the field of computer vision [2, 3]. Two security approaches are adopted by the researcher to defend the intruders from accessing those contents: Cryptography and Steganography. Cryptography, the process of transforming confidential data into non-readable form, provides secure data transfer although the encrypted form of data attracts attention and reveals it
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