Fuzzy edge detection based steganography using modified Gaussian distribution

Dhargupta, Souvik; Chakraborty, Anuran; Ghosal, Sudipta Kr; Saha, Shaswata; Sarkar, Ram

doi:10.1007/s11042-018-7123-x

Cited by 28 publications

(6 citation statements)

References 16 publications

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“…[12] proposed a method that is able to determine the precise edge areas of a cover image as well as ensure the exact edge locations after embedding the secret message based on a fuzzy inference system. In [26] a fuzzy edge detection is applied to the masked image to preserve the edge information; the edge pixels are embedded with more bits. If the pixel is neither an edge nor a background, the Gaussian function is used to decide the amount of data to be embedded and is based on the Euclidean distance between the current and the closest edge pixels.…”

Section: Related Workmentioning

confidence: 99%

Improved LSB image steganography with high imperceptibility based on cover‐stego matching

Al‐Faydi

Ahmed

Al‐Talb

2023

IET Image Processing

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Information security is an important factor when critical information is transferred. This paper concentrates on finding a method that can hide a message in an image without making any changes to it. The proposed method is novel and based on the Least Significant Bit (LSB) method with a high imperceptibility and capacity. Initially, the secret message bits are compressed using the LZW technique, then the compressed message bits are embedded into the LSB of the edge pixels in the cover image which have the same bit values. As a result, the cover image pixels remain unchanged and the stego image matches the cover image. The addresses of pixels that contain the compressed secret bits are maintained in a file called “location addresses” which the receiver uses to retrieve the compressed secret message. It is possible that the stego image will not be transmitted if it was previously specified by the sender and the receiver. Image quality metrics are applied to several standard images. The obtained PSNR for the stego image is infinity, SSIM and NCC are one, MSE and AD are zero for different embedding capacities in all images, which ensures the stego image's excellent imperceptibility.

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Section: Related Workmentioning

confidence: 99%

Improved LSB image steganography with high imperceptibility based on cover‐stego matching

Al‐Faydi

Ahmed

Al‐Talb

2023

IET Image Processing

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“…By using LSB methods, the capacity with high data can be achieved. Figure 1 shows various stego images of Lena in different embedding percentages [9]- [11].…”

Section: Preliminaries 21 Least-significant-bit Lsb Substitutionmentioning

confidence: 99%

Security and imperceptibility improving of image steganography using pixel allocation and random function techniques

Abbas¹,

Abdulredha²,

Ibrahim

et al. 2022

IJECE

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Information security is one of the main aspects of processes and methodologies in the technical age of information and communication. The security of information should be a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies that are used, and they include steganography and cryptography. An effective digital image-steganographic method based on odd/even pixel allocation and random function to increase the security and imperceptibility has been improved. This lately developed outline has been verified for increasing the security and imperceptibility to determine the existent problems. Huffman coding has been used to modify secret data prior embedding stage; this modified equivalent secret data that prevent the secret data from attackers to increase the secret data capacities. The main objective of our scheme is to boost the peak-signal-to-noise-ratio (PSNR) of the stego cover and stop against any attack. The size of the secret data also increases. The results confirm good PSNR values in addition of these findings confirmed the proposed method eligibility.

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“…The hidden media was then stashed away in the LSB plane. Dhargupta et al 9 suggested a steganography‐based fuzzy edge multimedia tools and applications identification technique that was based on a modified Gaussian distribution. Utilizing this method would allow the payload of the stego picture to be modified even once the stego image was generated thus allowing to add additional data if needed.…”

Section: Steganography: An Overviewmentioning

confidence: 99%

A survey of recent advances in image steganography

Kumar

Rani

Singh

2022

Security and Privacy

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Over the course of technological development, data security while transmission and storing has always been a field of continuous advancements and key importance. In the recent times, its importance has increased even further and the main reason behind this is the increase in the amount of data that is being transferred over internet and various networks. Although there are cryptographic approaches to handle these situations, steganography provides a different aspect to the solution. While cryptography encrypts the data such that the cipher text is visible to human eye, but the meaning of that encrypted text is never known. Steganography is the process of hiding data in plain sight inside some images, audio, video and so on, such that it is very difficult for the viewer to directly differentiate if there is some data hidden in the host or not. This paper consists a study of various methodologies to implement Image steganography like spatial domain manipulation, encryption based and deep learning approaches. The paper focuses upon recent advances in all these approaches for image steganography and aims to help other researches in this domain to understand recent developments as well future advancements possible in the same field. Furthermore, this paper illustrates about how different image formats effect various image steganography methods and how the various image color models interact with image steganography. Later this article sheds some light on the domain of steganalysis and reports about the fundamental principles as well as various implementations of steganalysis systems in recent times.

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Fuzzy edge detection based steganography using modified Gaussian distribution

Cited by 28 publications

References 16 publications

Improved LSB image steganography with high imperceptibility based on cover‐stego matching

Improved LSB image steganography with high imperceptibility based on cover‐stego matching

Security and imperceptibility improving of image steganography using pixel allocation and random function techniques

A survey of recent advances in image steganography

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