Image steganography based on Kirsch edge detection

Ghosal, Sudipta Kr; Chatterjee, Agneet; Sarkar, Ram

doi:10.1007/s00530-020-00703-3

Cited by 24 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…StegExpose iterates over a number of stages to only analyze potentially probable stego files in order to execute a quick fusion and save calculation time. A detector component is connected, and the cumulative arithmetic mean is updated at each step 10 . The image is considered manipulated if this mean falls below a specified threshold.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…A detector component is connected, and the cumulative arithmetic mean is updated at each step. 10 The image is considered manipulated if this mean falls below a specified threshold. Table 6 shows that test images are all under the stego threshold that is, the steganalysis fails the test because it does not detect the presence of stego images.…”

Section: Poof Of Securitymentioning

confidence: 99%

“…The constraint of this technique is that it cannot support 2 × 2 decomposition of blocks for DWT and DFT and 8 × 8 block‐based decompound for DCT. This scheme's 10 aim is to optimize the payload capacity by putting extra bits in edge pixel intensities and fewer bits in non‐edge regions. The Kirsch edge detector reconstructs the cover image, which melts into a series of triplets of pixels.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An efficient image steganography approach based on QR factorization and singular value decomposition in non‐subsampled contourlet transform domain

Yasmeen

Uddin

2022

Security and Privacy

View full text Add to dashboard Cite

As the amount of information communicated through the virtual world grows dramatically day by day, data security becomes a significant concern while communicating over the internet. Steganography can play an integral part in safeguarding the data from unauthorized users through a hiding mechanism. This article describes a three-step steganographic algorithm. This algorithm applied QR factorization and singular value decomposition (SVD) in the non-subsampled contourlet transform (NSCT) domain. First, this algorithm scrambles the secret image using Arnold transforms, and then NSCT decomposes the carrier and the scrambled hidden image into coefficient sub-bands. Second, the QR factorization and SVD are used on the specific coefficients of carrier and scrambled secret images, respectively. Finally, the modified secret image inserts into the carrier image for communication. The reverse mechanism is applied to discover the hidden message at the receiving end. The methodology gives superior imperceptibility as well as robustness. Furthermore, the anticipated strategy is compared with current methods in terms of effectiveness.

show abstract

Section: Experiments Resultsmentioning

confidence: 99%

Section: Poof Of Securitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An efficient image steganography approach based on QR factorization and singular value decomposition in non‐subsampled contourlet transform domain

Yasmeen

Uddin

2022

Security and Privacy

View full text Add to dashboard Cite

show abstract

“…However, the technique can be further enhanced in the security domain. In 2021, Ghosal et al [ 60 ] have suggested an image hiding approach based on the Kirsch edge detector. In this method, the data has been embedded into both edge and non-edge regions of an image in such a way that image distortion is minimized.…”

Section: Literature Surveymentioning

confidence: 99%

Double layer security using crypto-stego techniques: a comprehensive review

et al. 2021

View full text Add to dashboard Cite

Recent advancement in the digital technology and internet has facilitated usage of multimedia objects for data communication. However, interchanging information through the internet raises several security concerns and needs to be addressed. Image steganography has gained huge attention from researchers for data security. Image steganography secures the data by imperceptibly embedding data bits into image pixels with a lesser probability of detection. Additionally, the encryption of data before embedding provides double-layer protection from the potential eavesdropper. Several steganography and cryptographic approaches have been developed so far to ensure data safety during transmission over a network. The purpose of this work is to succinctly review recent progress in the area of information security utilizing combination of cryptography and steganography (crypto-stego) methods for ensuring double layer security for covert communication. The paper highlights the pros and cons of the existing image steganography techniques and crypto-stego methods. Further, a detailed description of commonly using evaluations parameters for both steganography and cryptography, are given in this paper. Overall, this work is an attempt to create a better understanding of image steganography and its coupling with the encryption methods for developing state of art double layer security crypto-stego systems.

show abstract

“…They used the renown LSB substitution method to implant data bits. In [29], Ghosal proposed a steganography scheme using the Kirsch edge detection method where they implanted the message bits into each triplet of pixels. Therefore, the embedding capacity is low.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Way of Hybridizing Edge Detectors Depending on Embedding Demand

Sultana¹,

Kamal²

2023

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Edge detection-based image steganography schemes usually embed data in edge pixels only. However, some schemes embed data in non-edge pixels as well. In that case, the schemes embed more bits in the edges than in the smoothed areas. In all cases, the schemes perform large changes in a tiny area of the image during small data embedding. Detecting such local modifications is comparatively easier for a steganalyzer. As a result, it is preferable to distribute bits evenly across the image. Again, the schemes struggle to hide large messages in a cover image due to the algorithmic approach of hiding a fixed number of bits per pixel. In this research, we have overcome that problem by employing multiple edge detectors in generating a resultant edge image. Depending on the embedding needs, we have checked whether a single edge detector is sufficient to help in conceiving all bits or not. If it is not possible for a single-edge detector, we have hybridized them. Hybridization of edge images is performed either by logical AND, OR or OR with dilation. When the message size is very small, we have generated the resultant edge image by doing a logical AND operation among the edge images. That strategy have reduced the number of edge pixels as well as helped in distributing the to-be-embedded bits over the image in a more evenly manner. Similarly, to meet a larger embedding demand, we have performed a logical OR operation among the same edge images to increase the number of edge pixels. Even, to meet more embedding demand, we have dilated the OR-resultant image. These processes were carried out dynamically in the research according to an embedding demand. The experimental results deduce that this scheme embeds 92.37%, 73.92%, 74.78%, and 9.60% more bits than four competing methods. Similarly, for small embedding demand, the proposed scheme demonstrates 37.45%, 46.87%, 44.21%, and 55.56% higher PSNR values than the others. Moreover, statistical analyses state that this scheme demonstrates stronger security against attacks.

show abstract

Image steganography based on Kirsch edge detection

Cited by 24 publications

References 22 publications

An efficient image steganography approach based on QR factorization and singular value decomposition in non‐subsampled contourlet transform domain

An efficient image steganography approach based on QR factorization and singular value decomposition in non‐subsampled contourlet transform domain

Double layer security using crypto-stego techniques: a comprehensive review

An Efficient Way of Hybridizing Edge Detectors Depending on Embedding Demand

Contact Info

Product

Resources

About