Steganography and Cryptography Techniques Based Secure Data Transferring Through Public Network Channel

Naser, Mohammed Abdullah; Al-Alak, Saif; Hussein, Ahmed Mohammed; Jawad, Majid Jabbar

doi:10.21123/bsj.2022.6142

Cited by 8 publications

(8 citation statements)

References 11 publications

(11 reference statements)

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“…The statistical analysis of visual characteristics and the temporal analysis of motion information have been suggested as robust methodologies. Color and texture may be used to segment a frame into many sections, which can then be combined based on motion vectors that are comparable to each other 8 . This is a usual method, and it can be used to a wide range of images.…”

Section: Object Detectionmentioning

confidence: 99%

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

2023

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Video steganography has become a popular option for protecting secret data from hacking attempts and common attacks on the internet. However, when the whole video frame(s) are used to embed secret data, this may lead to visual distortion. This work is an attempt to hide sensitive secret image inside the moving objects in a video based on separating the object from the background of the frame, selecting and arranging them according to object's size for embedding secret image. The XOR technique is used with reverse bits between the secret image bits and the detected moving object bits for embedding. The proposed method provides more security and imperceptibility as the moving objects are used for embedding, so it is difficult to notice the changes in the moving objects instead of using background area for embedding in the video. The experimental results showed the better visual quality of the stego video with PSNR values exceeding 58 dB, this indicates that the proposed method works without causing much distortion in the original video and transmitted secret message.

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Section: Object Detectionmentioning

confidence: 99%

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

2023

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“…The test requires a count of the number of repeated patterns, however, it does not require the frequency of each pattern. Naser [7] proposed an extension to the binary standard randomness tests that are suitable to be applied on penta-sequences (𝐺𝐹(5)), and he applied the extended randomness to the output of an example of the digital penta-sequences. Ibraheem [8] attempted to extend the 2-tuple to d-tuple (𝑑 ≥ 3) for the serial test, then we will generalize the 2-tuple binary serial test to 2-tuple digital serial test for the digital (𝑠 −) sequences (𝑠 ≥ 3) that are generated from digital generators.…”

Section: Introductionmentioning

confidence: 99%

Randomness of Stream Cipher Digital Sequences in the Field GF(28)

Ali,

Redha

2024

Iraqi Journal of Science

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A binary stream cipher cryptosystem can be used to encrypt/decrypt many types of digital files, especially those can be considered huge data files like images. To guarantee that the encryption or decryption processes need a reasonable time to encrypt/decrypt the images, so we have to make the stream cipher key generator that acts quickly without effect in the complexity or randomness of the output key binary sequences. In this paper, we increase the size of the output sequence from binary to digital sequence in the field to obtain byte sequence, then we test this new sequence not only as binary but also -sequence. So we have to test the new output sequence in the new mathematical field. This is done by changing the base of the randomness tests and extending Golomb’s postulates from binary to . Some theorems and lemmas are proved to find the new testing laws that are suitable to the new sequences field. The results of using the extended randomness tests are compared to the results of binary randomness tests to guarantee that the decision of pass or fail is identical, the results also prove the precision of identicalness.

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“…Methods of protecting information varied between encryption and steganography. Encryption is the conversion of confidential data into an unreadable format [1] which is a sprinkling of letters that do not give any meaning only those who possess the encryption keys can decipher the text and return it to an understandable format [2].…”

Section: Introductionmentioning

confidence: 99%

Randomized Information Hiding in RGB Images Using Genetic Algorithm and Huffman Coding

Hussein,

Al Baity,

Hadi

2023

RIA

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Protecting information from manipulation and theft is a top priority as a result of progress in technology and the infrastructure of the multimedia network in addition to the development of illegal methods of obtaining information. One of the means of protecting and preserving information is to hide it in a digital medium. The motivation for introducing such a system is to enhance the security of confidential data by providing ways to protect the data and reduce attempts to attack it. The proposed system contains several steps summarized as follows: The sender side includes firstly the stage of generating hiding locations randomly depending on the genetic algorithm (GA) to generate rows and seed to generate columns. Secondly, the stage of including data after compressing it by the Huffman method. Data embedding depends on the pixel index as an indicator to choose one of the three bands to hide using LSB. The recipient side extracts the important information hiding in the two last rows which helps to extract the data and convert it into the original text. The proposed system gained efficiency and robustness with the help of genetic and Huffman where genetic chooses the best way for hidding among a set of suggested solution in addition to the randomness it possesses. The role of Huffman reduce data size and thus increase the cover capacity. System efficiency has been measured by PSNR through conducting a number of experiments that included using set of texts with different sizes and two types of standard colored cover images.

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Steganography and Cryptography Techniques Based Secure Data Transferring Through Public Network Channel

Cited by 8 publications

References 11 publications

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

Randomness of Stream Cipher Digital Sequences in the Field GF(28)

Randomized Information Hiding in RGB Images Using Genetic Algorithm and Huffman Coding

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