A highly secure video steganography using Hamming code (7, 4)

Mstafa, Ramadhan J.; Elleithy, Khaled

doi:10.1109/lisat.2014.6845191

Cited by 48 publications

(26 citation statements)

References 7 publications

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“…Hence, researchers have to develop secure steganography algorithms that are protected from both attackers and steganalysis detectors. Any successful steganography system should consider two main important factors: embedding payload and embedding efficiency [2].…”

Section: Introductionmentioning

confidence: 99%

A DCT-based robust video steganographic method using BCH error correcting codes

Mstafa

Elleithy

2016

2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT)

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Due to the significant growth of video data over the Internet, it has become a popular choice for data hiding field. The performance of any steganographic algorithm relies on the embedding efficiency, embedding payload, and robustness against attackers. Low hidden ratio, less security, and low quality of stego videos are the major issues of many existing steganographic methods. In this paper, we propose a DCT-based robust video steganographic method using BCH codes. To improve the security of the proposed algorithm, a secret message is first encrypted and encoded by using BCH codes. Then, it is embedded into the discrete cosine transform (DCT) coefficients of video frames. The hidden message is embedded into DCT coefficients of each Y, U, and V planes excluding DC coefficients. The proposed algorithm is tested under two types of videos that contain slow and fast moving objects. The results of the proposed algorithm are compared with three existing methods. The results demonstrate better performance for the proposed algorithm than for the others. The hidden ratio of the proposed algorithm is approximately 27.53%, which is evaluated as a high hiding capacity with a minimal tradeoff of the visual quality. The robustness of the proposed algorithm was tested under different attacks. areas of wireless sensor networks, mobile communications, network security, quantum computing, and formal approaches for design and verification. He has published more than three hundred research papers in international journals and conferences in his areas of expertise. Dr. Elleithy has more than 25 years of teaching experience. His teaching evaluations are distinguished in all the universities he joined. He supervised hundreds of senior projects, MS theses and Ph.D. dissertations. He supervised several Ph.D. students. He developed and introduced many new undergraduate/graduate courses. He also developed

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Section: Introductionmentioning

confidence: 99%

A DCT-based robust video steganographic method using BCH error correcting codes

Mstafa

Elleithy

2016

2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT)

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“…Information and parity bits can be mixed together in different ways [23]. In this paper, parity bits are put at the beginning of information bits.…”

Section: Hamming Encodermentioning

confidence: 99%

Performance analysis of Hamming code for WSN-based smart grid applications

Yiğit¹,

Güngör²,

Boluk³

2018

Turk J Elec Eng & Comp Sci

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Many methods have been employed to detect, compare, and correct errors to increase communication reliability and efficiency in wireless sensor networks (WSNs). However, to the best of our knowledge, no existing study has compared the performance of error control codes by using different modulation techniques in a smart grid communication environment when multichannel scheduling is used. This paper presents a detailed performance evaluation and makes a comparison of different modulation techniques, such as frequency shift keying (FSK), differential phase shift keying (DPSK), binary phase shift keying (BPSK), and offset quadrature phase-shift keying (OQPSK), using Hamming codes in a 500-kV line-of-sight substation smart grid environment with multichannel scheduling. A link-quality-aware routing algorithm is used as a routing protocol and a log-normal shadowing channel is employed as a channel model. Simulations are performed in MATLAB and the performance of the Hamming code with various modulation techniques is compared with the results obtained without using any error correction codes for throughput, delay, and bit error rate. The results show that the performance of the Hamming code with OQPSK modulation is better than its performance with other modulation techniques. Moreover, the results show that the performance of Hamming code improves with multichannel scheduling for all modulation techniques.

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“…If noise (4) exists, then (3) is called noisy channel and there is a probability of errors which could change the encoded message. After encoded message is received, it will be decoded and corrected if possible in (5), and finally the decoded message will arrive at sink (6). Like on error-detecting codes, we need to add some additional check digits to the message such that any changes made to the other digits will be detected and corrected properly.…”

Section: Parity Check Encodermentioning

confidence: 99%

“…This idea leads to error-correcting codes, which has the abilities of detecting and correcting errors, provided they are less or equal to correcting radius of the code [4]. Some applications of coding theory was shown by [5] video steganography using Hamming code, [6] on lossless data compression, [7] on development of Hamming codes as error-reducing codes, and [8] on biomedical engineering's medical image authentication using Hamming code. In this paper we will use two types of data for simulations, namely vector and text data.…”

Section: Introductionmentioning

confidence: 99%

Simulations of linear and Hamming codes using SageMath

Timur

Adzkiya

Soleha

2018

J. Phys.: Conf. Ser.

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Abstract. Digital data transmission over a noisy channel could distort the message being transmitted. The goal of coding theory is to ensure data integrity, that is, to find out if and where this noise has distorted the message and what the original message was. Data transmission consists of three stages: encoding, transmission, and decoding. Linear and Hamming codes are codes that we discussed in this work, where encoding algorithms are parity check and generator matrix, and decoding algorithms are nearest neighbor and syndrome. We aim to show that we can simulate these processes using SageMath software, which has built-in class of coding theory in general and linear codes in particular. First we consider the message as a binary vector of size k. This message then will be encoded to a vector with size n using given algorithms. And then a noisy channel with particular value of error probability will be created where the transmission will took place. The last task would be decoding, which will correct and revert the received message back to the original message whenever possible, that is, if the number of error occurred is smaller or equal to the correcting radius of the code. In this paper we will use two types of data for simulations, namely vector and text data.

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A highly secure video steganography using Hamming code (7, 4)

Cited by 48 publications

References 7 publications

A DCT-based robust video steganographic method using BCH error correcting codes

A DCT-based robust video steganographic method using BCH error correcting codes

Performance analysis of Hamming code for WSN-based smart grid applications

Simulations of linear and Hamming codes using SageMath

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