Steganalysis and payload estimation of embedding in pixel differences using neural networks

Sabeti, Vajiheh; Samavi, Shadrokh; Mahdavi, Mojtaba; Shirani, Shahram

doi:10.1016/j.patcog.2009.06.006

Cited by 41 publications

(14 citation statements)

References 17 publications

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“…In this method, the stego image quality is improved a lot but data hiding capacity is not. Another important method [13] which also exploits the characteristics of the human visual system and makes use of both pixel value differencing (PVD) and least significant bit (LSB) substitution scheme is discussed. In this method, the image is also partitioned into non-overlapping blocks of two consecutive pixels and then the difference value is computed for each block in a similar way as in [12].…”

Section: Fig 1 Steganography Techniquementioning

confidence: 99%

See 1 more Smart Citation

A Modified Pixel-Value Differencing Image Steganographic Scheme with Least Significant Bit Substitution Method

Malik¹,

Sikka²,

Verma³

2015

IJIGSP

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Pixel value differencing is a steganographic technique for gray scaled images. In this paper, we propose a modified pixel value differencing image steganographic scheme with least significant bit substitution method. Our method divides the cover image into the blocks of two consecutive pixels and calculates the absolute difference between the pixels of a block similar to [1, 2]. If the difference is less than a particular threshold, i.e. 15 (in this paper) than 4 bits of secret data are taken and these bits are embedded onto the LSBs of the block's pixels through least significant bit substitution method otherwise the number of bits to be hidden are selected based on some characteristics of the block and hidden. The experimental results show that our method significantly improves the quality of stego image as compared to the [1, 3] and have sufficient payload.

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Section: Fig 1 Steganography Techniquementioning

confidence: 99%

“…Thus, it maximizes the data hiding capacity to a great extent even without disturbing the image quality much. The method discussed in [12,13] identify the horizontal edges only. However, these methods do not perform to the expectations.…”

Section: Fig 1 Steganography Techniquementioning

confidence: 99%

A Modified Pixel-Value Differencing Image Steganographic Scheme with Least Significant Bit Substitution Method

Malik¹,

Sikka²,

Verma³

2015

IJIGSP

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show abstract

“…In (Sabeti et al, 2010), the authors presented Five different N-level perceptron neural network (NN) approaches trained to discover diverse layers of implanting. Every image is served to all systems and electing scheme classifies the image as either cover images or stego images.…”

Section: Neural Network Classification (Nn)mentioning

confidence: 99%

A Survey of Data Mining Techniques for Steganalysis

Mohammadi¹,

Abadeh²

2012

Recent Advances in Steganography

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“…Spatial domain-based algorithms conceal secret message straightforwardly in the intensity of pixels of an image, while in frequency domain-based algorithms, the image is firstly transformed into its frequency domain and secret message is then concealed in the transform coefficients [19], [20].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Steganography System based on LSB Matching and Replacement

Hiary¹,

Sabri²,

Mohammed³

et al. 2016

ijacsa

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Abstract-This paper proposes a hybrid steganographic approach using the least significant bit (LSB) technique for grayscale images. The proposed approach uses both LSB matching (LSB-M) and LSB replacement to hide the secret data in images. Using hybrid LSB techniques increase the level of security. Thus, attackers cannot easily, if not impossible, extract the secret data. The proposed approach stores two bits in a pixel. The embedding rate can reach up to 1.6 bit per pixel. The proposed approach is evaluated and subjected to various kinds of image processing attacks. The performance of the proposed algorithm is compared with two other relevant techniques; pixelvalue differencing (PVD) and Complexity Based LSB-M (CBL). Experimental results indicate that the proposed algorithm outperforms PVD in terms of imperceptibility. Also, it significantly outperforms CBL in two main features; higher embedding rate (ER), and more robust to most common image processing attacks such as median filtering, histogram equalization, and rotation.

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Steganalysis and payload estimation of embedding in pixel differences using neural networks

Cited by 41 publications

References 17 publications

A Modified Pixel-Value Differencing Image Steganographic Scheme with Least Significant Bit Substitution Method

A Modified Pixel-Value Differencing Image Steganographic Scheme with Least Significant Bit Substitution Method

A Survey of Data Mining Techniques for Steganalysis

A Hybrid Steganography System based on LSB Matching and Replacement

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