5 research outputs found
An Efficient Light-weight LSB steganography with Deep learning Steganalysis
Active research is going on to securely transmit a secret message or
so-called steganography by using data-hiding techniques in digital images.
After assessing the state-of-the-art research work, we found, most of the
existing solutions are not promising and are ineffective against machine
learning-based steganalysis. In this paper, a lightweight steganography scheme
is presented through graphical key embedding and obfuscation of data through
encryption. By keeping a mindset of industrial applicability, to show the
effectiveness of the proposed scheme, we emphasized mainly deep learning-based
steganalysis. The proposed steganography algorithm containing two schemes
withstands not only statistical pattern recognizers but also machine learning
steganalysis through feature extraction using a well-known pre-trained deep
learning network Xception. We provided a detailed protocol of the algorithm for
different scenarios and implementation details. Furthermore, different
performance metrics are also evaluated with statistical and machine learning
performance analysis. The results were quite impressive with respect to the
state of the arts. We received 2.55% accuracy through statistical steganalysis
and machine learning steganalysis gave maximum of 49.93~50% correctly
classified instances in good condition.Comment: Accepted pape
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
Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods
This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read
Improved modification direction methods
AbstractThe original exploiting modification direction (EMD) method proposed by Zhang and Wang is a novel data hiding technique which can achieve large embedding capacity with less distortion. The original EMD method can hide (2n+1)-ary numbers by modifying at most one least-significant bit (LSB) of n pixel values. The proposed methods in this paper, 2-EMD and EMD-2, modify at most two pixels of the LSB values. Efficiency of the proposed methods is shown theoretically and through experiments. The 2-EMD and EMD-2 can hide even larger numbers than the EMD with similar distortion under the same conditions. This paper shows that the EMD-2 is much better than the EMD, and slightly better than 2-EMD when n is 3, 4 and 5. The way to generate basis vector can be used for the generalization of the n-EMD and EMD-n where n>1