220 research outputs found
Steganography Approach to Image Authentication Using Pulse Coupled Neural Network
This paper introduces a model for the authentication of large-scale images. The crucial element of the proposed model is the optimized Pulse Coupled Neural Network. This neural network generates position matrices based on which the embedding of authentication data into cover images is applied. Emphasis is placed on the minimalization of the stego image entropy change. Stego image entropy is consequently compared with the reference entropy of the cover image. The security of the suggested solution is granted by the neural network weights initialized with a steganographic key and by the encryption of accompanying steganographic data using the AES-256 algorithm. The integrity of the images is verified through the SHA-256 hash function. The integration of the accompanying and authentication data directly into the stego image and the authentication of the large images are the main contributions of the work
Tampering with a watermarking-based image authentication scheme
We analyse a recent image authentication scheme designed by Chang et al. [A watermarking-based image ownership and tampering authentication scheme, Pattern Recognition Lett. 27 (5) (2006) 439–446] whose first step is based on a watermarking scheme of Maniccam and Bourbakis [Lossless compression and information hiding in images, Pattern Recognition 37 (3) (2004) 475–486]. We show how the Chang et al. scheme still allows pixels to be tampered, and furthermore discuss why its ownership cannot be uniquely binding. Our results indicate that the scheme does not achieve its designed objectives of tamper detection and image ownership
Towards Optimal Copyright Protection Using Neural Networks Based Digital Image Watermarking
In the field of digital watermarking, digital image watermarking for copyright protection has attracted a lot of attention in the research community. Digital watermarking contains varies techniques for protecting the digital content. Among all those techniques,Discrete Wavelet Transform (DWT) provides higher image imperceptibility and robustness. Over the years, researchers have been designing watermarking techniques with robustness in mind, in order for the watermark to be resistant against any image processing techniques. Furthermore, the requirements of a good watermarking technique includes a tradeoff between robustness, image quality (imperceptibility) and capacity. In this paper, we have done an extensive literature review for the existing DWT techniques and those combined with other techniques such as Neural Networks. In addition to that, we have discuss the contribution of Neural Networks in copyright protection. Finally we reached our goal in which we identified the research gaps existed in the current watermarking schemes. So that, it will be easily to obtain an optimal techniques to make the watermark object robust to attacks while maintaining the imperceptibility to enhance the copyright protection
Digital watermarking by utilizing the properties of self-organization map based on least significant bit and most significant bit
Information security is one of the most important branches concerned with maintaining the confidentiality and reliability of data and the medium for which it is transmitted. Digital watermarking is one of the common techniques in this field and it is developing greatly and rapidly due to the great importance it represents in the field of reliability and security. Most modern watermarking systems, however, use the self-organization map (SOM), which is safer than other algorithms because an unauthorized user cannot see the result of the SOM's training. Our method presents a semi-fragile watermark under spatial domain using least significant bit (LSB) and by relying on most significant bit (MSB) when the taken values equal to (2 or 4 bits) depending on the characteristics of SOM through developing the so-called best matching unit (BMU) which working to determine the best location for concealment. As a result, it shows us the ability of the proposed method to maintain the quality of the host with the ability to retrieve data, whether it is a binary image or a secret message
Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM
The rapid growth of Internet and the fast emergence of multi-media applications over the past decades have led to new problems such as illegal copying, digital plagiarism, distribution and use of copyrighted digital data. Watermarking digital data for copyright protection is a current need of the community. For embedding watermarks, robust algorithms in die media will resolve copyright infringements. Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized. In this paper, the optimized Discrete Wavelet Transform (DWT) is utilized for embedding the watermark. The optimization algorithm is a combination of Simulated Annealing (SA) and Tunicate Swarm Algorithm (TSA). After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM). From the extraction process, the original image is obtained by this RNN-LSTM method. The experimental set up is carried out in the MATLAB platform. The performance metrics of PSNR, NC and SSIM are determined and compared with existing optimization and machine learning approaches. The results are achieved under various attacks to show the robustness of the proposed work
Recent Advances in Steganography
Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced
Digital watermark technology in security applications
With the rising emphasis on security and the number of fraud related crimes
around the world, authorities are looking for new technologies to tighten
security of identity. Among many modern electronic technologies, digital
watermarking has unique advantages to enhance the document authenticity.
At the current status of the development, digital watermarking technologies
are not as matured as other competing technologies to support identity authentication
systems. This work presents improvements in performance of
two classes of digital watermarking techniques and investigates the issue of
watermark synchronisation.
Optimal performance can be obtained if the spreading sequences are designed
to be orthogonal to the cover vector. In this thesis, two classes of
orthogonalisation methods that generate binary sequences quasi-orthogonal
to the cover vector are presented. One method, namely "Sorting and Cancelling"
generates sequences that have a high level of orthogonality to the
cover vector. The Hadamard Matrix based orthogonalisation method, namely
"Hadamard Matrix Search" is able to realise overlapped embedding, thus the
watermarking capacity and image fidelity can be improved compared to using
short watermark sequences. The results are compared with traditional
pseudo-randomly generated binary sequences. The advantages of both classes
of orthogonalisation inethods are significant.
Another watermarking method that is introduced in the thesis is based
on writing-on-dirty-paper theory. The method is presented with biorthogonal
codes that have the best robustness. The advantage and trade-offs of
using biorthogonal codes with this watermark coding methods are analysed
comprehensively. The comparisons between orthogonal and non-orthogonal
codes that are used in this watermarking method are also made. It is found
that fidelity and robustness are contradictory and it is not possible to optimise
them simultaneously.
Comparisons are also made between all proposed methods. The comparisons
are focused on three major performance criteria, fidelity, capacity and
robustness. aom two different viewpoints, conclusions are not the same. For
fidelity-centric viewpoint, the dirty-paper coding methods using biorthogonal
codes has very strong advantage to preserve image fidelity and the advantage
of capacity performance is also significant. However, from the power
ratio point of view, the orthogonalisation methods demonstrate significant
advantage on capacity and robustness. The conclusions are contradictory
but together, they summarise the performance generated by different design
considerations.
The synchronisation of watermark is firstly provided by high contrast
frames around the watermarked image. The edge detection filters are used
to detect the high contrast borders of the captured image. By scanning
the pixels from the border to the centre, the locations of detected edges
are stored. The optimal linear regression algorithm is used to estimate the
watermarked image frames. Estimation of the regression function provides
rotation angle as the slope of the rotated frames. The scaling is corrected by
re-sampling the upright image to the original size. A theoretically studied
method that is able to synchronise captured image to sub-pixel level accuracy
is also presented. By using invariant transforms and the "symmetric
phase only matched filter" the captured image can be corrected accurately
to original geometric size. The method uses repeating watermarks to form an
array in the spatial domain of the watermarked image and the the array that
the locations of its elements can reveal information of rotation, translation
and scaling with two filtering processes
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