299 research outputs found
Improving success probability and embedding efficiency in code based steganography
For stegoschemes arising from error correcting codes, embedding depends on a
decoding map for the corresponding code. As decoding maps are usually not
complete, embedding can fail. We propose a method to ensure or increase the
probability of embedding success for these stegoschemes. This method is based
on puncturing codes. We show how the use of punctured codes may also increase
the embedding efficiency of the obtained stegoschemes
Increasing Embedding Efficiency & Security of Extended Matrix Encoding Algorithm by Providing Compression & Encryption
Extended Matrix Encoding Algorithm is totally different from most of the LSB replacement or matching steganographic schemes. With reducing the amount of necessary changes the extended matrix algorithm is used to increase embedding efficiency. By using this algorithm, the hidden message is inserted into carrier media and can be transferred via safer channel. In this algorithm the quantitative DCT coefficients of JPEG image which makes the data safe from visual attack. The embedding efficiency and embedding rate get increased to large extent by changing the hash function in matrix encryption and changing the coding mode. In this paper I am trying to show that we can increase embedding efficiency by compressing the secrete data also increase the security by encryption and provision of double password.
DOI: 10.17762/ijritcc2321-8169.15075
Introducing a New Evaluation Criteria for EMD-Base Steganography Method
Steganography is a technique to hide the presence of secret communication.
When one of the communication elements is under the influence of the enemy, it
can be used. The main measure to evaluate steganography methods in a certain
capacity is security. Therefore, in a certain capacity, reducing the amount of
changes in the cover media, creates a higher embedding efficiency and thus more
security of an steganography method. Mostly, security and capacity are in
conflict with each other, the increase of one lead to the decrease of the
other. The presence of a single criterion that represents security and capacity
at the same time be useful in comparing steganography methods. EMD and the
relevant methods are a group of steganography techniques, which optimize the
amount of changes resulting from embedding (security). The present paper is
aimed to provide an evaluation criterion for this group of steganography
methods. In this study, after a general review and comparison of EMD-based
steganography techniques, we present a method to compare them exactly, from the
perspective of embedding efficiency. First, a formula is presented to determine
the value of embedding efficiency, which indicates the effect of one or more
changes on one or more pixels. The results demonstrate that the proposed
embedding efficiency formula shows the performance of the methods better when
several changes are made on a pixel compared to the existing criteria. In the
second step, we have obtained an upper bound, which determines the best
efficiency for each certain capacity. Finally, based on the introduced bound,
another evaluation criterion for a better comparison of the methods is
presented
Pseudo-random number generators and an improved steganographic algorithm
Steganography is the art and science of hiding secret information in a cover medium such that the presence of the hidden information cannot be detected. This thesis proposes a new method of steganography by cover modification in JPEG images. Essentially, the algorithm exercises LSB replacement using the definition for steganographic values from F5. After the nonzero quantized DCT coefficients of a cover image undergo a pseudorandom walk, the coefficients and the payload are split into an equal number of partitions and paired. Each coefficient partition is permuted again by the 1/P pseudo-random number generator until an optimal embedding efficiency for its corresponding payload is achieved. Using this method, we achieve a higher embedding efficiency than that of LSB replacement alone. We evaluate the detectability of our algorithm by creating a multi-classifier based on the output of multiple non-linear, soft-margin support vector machines trained on POMM features. We show that our algorithm performs nearly as well as the state-of-the-art nsF5 algorithm, and outperforms other state-of-the-art algorithms under most conditions
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