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Image pairings formed for the experiment done with the proposed steganography algorithm. Cover images: (a) boat (1024 x 768 pixels); (c) sphinx (1024 x 768 pixels); (e) paradise (1024 x 768 pixels); (g) prayer (1024 x 768 pixels); (i) mountain (1024 x 768 pixels); (k) fire (1024 x 768 pixels); (m) wood fire (1024 x 768 pixels); and (o) lena (256 x 256 pixels). Images to be hidden: (b) Agfa (712 x 534 pixels); (d) snow (712 x 534 pixels); (f) desert (1024 x 768 pixels); (h) skullcap (1024 x 768 pixels); (j) landscape (1024 x 768 pixels); (l) mosque (1024 x 768 pixels); (n) cactus (576 x 768 pixels); and (p) mandrill (256 x 256 pixels).
Source publication
In recent years, thanks to the use of Internet services, daily activities used to imply movement became more accessible to any user. As a result of such interconnection, now millions of people from different countries are able to communicate among themselves through the Internet, generating a great flow of data and classified information. The infor...
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Citations
... PST also introduces higher performance than; (1) using the block partitioning principle , and (2) using fuzzy neural networks in Saleema and Amarunnishad (2016) as they cause degradation in the quality of stego-image. On the other hand, correlation values of Blanca and Manuel (2018) are acceptable which means that the proposed nonlocal maximum likelihood filters is good for implementing in steganography process. Table 14 represents the entropy values of PST and other techniques, which are desired to be as closed to 8 as possible. ...
... Table 14 represents the entropy values of PST and other techniques, which are desired to be as closed to 8 as possible. It is clear that PST introduces Saleema and Amarunnishad (2016) 7.6913 7.7180 Blanca and Manuel (2018) 7.7152 7.6692 Gandharba (2018) 7.5250 7.5413 Anita et al. (2018) 7.6282 7.7034 the highest entropy values compared to other techniques as it's values are closed to the maximum value (e.g. 8). This indicates the high amount of information in the stego-image and this identifies its accuracy after implementing the steganography process. ...
... This indicates the high amount of information in the stego-image and this identifies its accuracy after implementing the steganography process. For techniques introduced in Saleema and Amarunnishad (2016), Blanca and Manuel (2018), Gandharba (2018) the results are also acceptable but their entropy values are less than those of PST. The shortest processing time is achieved by PST as shown in Table 15. ...
Fog computing has become a fast intermediate between the cloud and the internet of things (IOT) devices. So, the communication channel between the fog and IOT devices must be secured against attacks. Both Cryptography and steganography are well-known techniques for data security. Each of them has its own advantages and strength points, hence, combining Cryptography and steganography in a hybrid system certainly promotes the system’s level of security. Although several hybrid security strategies have been proposed, they suffer from many drawbacks especially when they are applied in fog environments. Fog computing requires not only high security level but also a real time transmission. Hence, recent hybrid security strategies cannot be applied directly in fog environments as they suffer from long processing time. This paper introduces a new Fog Based Security Strategy (FBS²). FBS² is a hybrid security strategy as it combines Cryptography and steganography techniques. The proposed Cryptography Technique (PCT) is divided into two phases, namely; confusion and diffusion. The former scrambles the secret image pixels, while the latter changes the pixels values using new confusion and diffusion methodologies respectively. Confusion divides the secret images into closed loops of pixels, then rearrange the pixels into straight paths, while diffusion employs a Programmable Linear Feedback Shift Register (PLFSR). On the other hand, the Proposed Steganography Technique is implemented using the discrete wavelet packet transform. Embedding process depends on a new matching procedure, which is based on the most significant bits of both the encrypted secret image and the corresponding employed cover image pixels. Least significant bits (LSBs) are used as indicators to the matching process. At first an intermediate code is derived from the matching process, then, it is encrypted to represent the LSBs of the stego-image. Experimental results have shown that the proposed FBS² outperforms the previous counterparts in terms of efficiency, security, and processing time, which proves its suitability for application on the fog.
... PST also introduces higher performance than; (1) using the block partitioning principle , and (2) using fuzzy neural networks in Saleema and Amarunnishad (2016) as they cause degradation in the quality of stego-image. On the other hand, correlation values of Blanca and Manuel (2018) are acceptable which means that the proposed nonlocal maximum likelihood filters is good for implementing in steganography process. Table 14 represents the entropy values of PST and other techniques, which are desired to be as closed to 8 as possible. ...
... Table 14 represents the entropy values of PST and other techniques, which are desired to be as closed to 8 as possible. It is clear that PST introduces (2016) 0.8989 0.8999 Blanca and Manuel (2018) 0.9563 0.9897 Gandharba (2018) 0.9312 0.9164 Anita et al. (2018) 0.9498 0.9445 Saleema and Amarunnishad (2016) 7.6913 7.7180 Blanca and Manuel (2018) 7.7152 7.6692 Gandharba (2018) 7.5250 7.5413 Anita et al. (2018) 7.6282 7.7034 the highest entropy values compared to other techniques as it's values are closed to the maximum value (e.g. 8). This indicates the high amount of information in the stego-image and this identifies its accuracy after implementing the steganography process. ...
... Table 14 represents the entropy values of PST and other techniques, which are desired to be as closed to 8 as possible. It is clear that PST introduces (2016) 0.8989 0.8999 Blanca and Manuel (2018) 0.9563 0.9897 Gandharba (2018) 0.9312 0.9164 Anita et al. (2018) 0.9498 0.9445 Saleema and Amarunnishad (2016) 7.6913 7.7180 Blanca and Manuel (2018) 7.7152 7.6692 Gandharba (2018) 7.5250 7.5413 Anita et al. (2018) 7.6282 7.7034 the highest entropy values compared to other techniques as it's values are closed to the maximum value (e.g. 8). This indicates the high amount of information in the stego-image and this identifies its accuracy after implementing the steganography process. ...
