Robust and secured image-adaptive data hiding

Mali, Suresh N.; Patil, Pradeep M.; Jalnekar, Rajesh

doi:10.1016/j.dsp.2011.09.003

Cited by 46 publications

(20 citation statements)

References 10 publications

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“…This layer is introduced to make it more difficult to detect the segments' edges by a steganalyser, and it is more secure to carry the secret message than to use one-level non-uniform segmentation (EL-Emam and AL-Zubidy, 2013;Mali, Suresh N. et al, 2012).…”

Section: Definition 23mentioning

confidence: 99%

New data-hiding algorithm based on adaptive neural networks with modified particle swarm optimization

El-Emam

2015

Computers & Security

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Section: Definition 23mentioning

confidence: 99%

New data-hiding algorithm based on adaptive neural networks with modified particle swarm optimization

El-Emam

2015

Computers & Security

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“…It is shown in Figure 18 that PSNR of six stego images are more than 33.2047 dB in the case of embedding intensity adjustment factor s≤ 8. Reference [16] hides one secret bit into two neighboring DCT mid frequency coefficients, so it can embed 77 824 bits if taking those coefficients, whose sequence number belongs in [6,42] after ZigZag scan as mid frequency coefficients, and it would get an acceptable image quality of stego image about 30 dB. Our proposed algorithm's capacity, robustness, and invisibility all improved compared with the algorithm proposed by [16], and meanwhile, it can achieve a good balance of invisibility, robustness, and capacity.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…The first type algorithms pay more attention to embedding capacity, such as those proposed by [11][12][13], the second type algorithms are absorbed in robustness, such as those proposed by [15][16][17][18]22], and the third type algorithms emphasize a compromise of capacity, robustness, and invisibility, such as those in [19][20][21]. It cannot say that the capacity of our proposed algorithm is as good as the first type and the robustness is as good as the second type because it belongs to the third type.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…The hiding algorithm based on LSB proposed by [12,14] that based on MSB in [13] and that based on de-clustering in [11], all have large capacity, but the robustness is not satisfactory. The hiding algorithm based on DWT in [15] and those based on discrete cosine transform (DCT) in [16][17][18] have good robustness and good invisibility; however, the data hiding capacity is very low because it is not originally designed for this purpose of large amount. It has become a hot spot on how to balance the invisibility, robustness, and capacity of the hiding algorithm.…”

Section: Motivationmentioning

confidence: 99%

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An image information hiding algorithm based on grey system theory

Huang

Chen

et al. 2013

Int J Communication

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SUMMARYOn the basis of grey system theory, an image information hiding algorithm is proposed in this paper. First, to choose the blocks of rich texture, a method based on two-dimensional grey relational analysis is designed; making use of the improved GM(1,1) prediction model, the secret image is compressed losslessly, and then scrambled by means of time-division multiplexing method in communication theory. Block discrete cosine transform is applied to the chosen blocks, and coefficients of mid frequencies are chosen as the embedded data, whereas the embedding intensity is determined on the basis of human visual system, and the embedding capacity of sub-blocks can be controlled by its two-dimensional grey relational grade adaptively. The experiment results show that the algorithm cannot only achieve satisfactory invisibility and robustness, but also improve the payload of the cover image, and meanwhile solve the problem that the small capacity of traditional algorithms in transform domain efficiently. Copyright © 2013 John Wiley & Sons, Ltd. , which takes the uncertain, little sample, poor information system as the research object, presented by Chinese scholar Julong Deng as an emerging discipline. Its contents mainly include grey prediction model, grey relational analysis, grey decision-making, and so forth. Information hiding technology is one of its important applied fields [2,3].Information hiding model was derived from 'prisoners problem', put forward by Simmons in 1984. To deliver information secretly, traditional cryptography [4, 5] mainly use replacing or scrambling technologies to disrupt the original features of the secret information. Just because cipher texts resemble a stream of meaningless codes, they easily attract attacker to try to either recover them or simply destroy them, which seriously affect the security of information communication. To circumvent this problem, information hiding technology [6-8] mainly focus on how to hide the coded information into another host media, which makes it hard for attackers to find out the secret information. And, it is also impossible to detect the carrier from vast amounts of data in communication channel. These characteristics make it widely accepted in information security fields.[9] provides one efficient means of concealing the authentication messages exchanged

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“…The IQM scores are computed from images and their Gaussian filtered versions with 5 . 0   and mask size 3×3 [25,26]. The variations in IQMs for proposed method, D. Wu, and B. Lai methods with embedding the 30000 bytes in cover images were considered.…”

Section: Security Analysis Of Proposed Methodsmentioning

confidence: 99%