Steganography using reversible texture synthesis
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Our approach for steganography uses reversible texture synthesis to conceal secret messages. A smaller source texture is resampled to synthesize a new texture image of arbitrary size, embedding messages during this process. This allows extraction of both the secret messages and original source texture from the synthesized stego texture. Our scheme offers variable embedding capacity proportional to the stego texture size, resistance to steganalysis, and reversible recovery of the source texture.
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Steganography using reversible texture synthesis
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Steganography using reversible texture synthesis
The document proposes a novel steganography approach using reversible texture synthesis. It conceals secret messages by embedding them during a texture synthesis process rather than in an existing cover image. This allows extraction of the secret messages and original source texture. The approach offers high embedding capacity proportional to the stego image size, is difficult to detect through steganalysis, and allows recovery of the source texture. Experimental results verified it can provide variable embedding capacities while producing visually plausible textures that recover the source.
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Steganography Using Reversible Texture Synthesis
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website:1croreprojects.com
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Steganography Using Reversible Texture Synthesis
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It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
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1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
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the embedding rate of 1 bit per pixel. The method takes 2-bits of the secret message at a time
and compares them to the LSBs of the two chosen pixel values for embedding, it always assumes
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index of the mismatch. It is shown that the proposed method outperforms the security of LSB
replacement, LSB matching, and LSB matching revisited by reducing the probability of
detection with their current targeted steganalysis methods. Other advantages of the proposed
method are reducing the overall bit-level changes to the cover image for the same amount of
embedded data and avoiding complex calculations. Finally, the new method results in little
additional distortion in the stego image, which could be tolerated.
M ESH S IMPLIFICATION V IA A V OLUME C OST M EASURE
We develop a polygonal mesh simplification algorithm based on a novel analysis of the mesh
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-
hyperbolic depend
-
ing
upon their discrete local geometry. Subsequently, the simplification process computes a volume cost for
each non
-
hyperbolic vertex, in anal
-
ogy with spherical volume, to capture the loss of fidelity if that vertex
is decimated. Vertices of least volume cost are then successively deleted and the resulting holes re
-
triangulated using a method based on a novel heuristic. Preliminary experiments i
ndicate a performance
comparable to that of the best known mesh simplification algorithms
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This paper proposes a steganalysis technique for both grayscale and color images. It uses the feature vectors derived from gray level co-occurrence matrix (GLCM) in spatial domain, which is sensitive to data embedding process. This GLCM matrix is derived from an image. Several combinations of diagonal elements of GLCM are considered as features. There is difference between the features of stego and non-stego images and this characteristic is used for steganalysis. Distance measures like Absolute distance, Euclidean distance and Normalized Euclidean distance are used for classification. Experimental results demonstrate that the proposed scheme outperforms the existing steganalysis techniques in attacking LSB steganographic schemes applied to spatial domain.
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This document provides an outline for a talk on Gaussian Process Latent Variable Models (GPLVM). It begins with an introduction to why latent variable models are useful for dimensionality reduction. It then defines latent variable models and shows their graphical model representation. The document reviews PCA and introduces probabilistic versions like Probabilistic PCA (PPCA) and Dual PPCA. It describes how GPLVM generalizes these approaches using Gaussian processes. Examples applying GPLVM to face and motion data are provided, along with practical tips and an overview of GPLVM variants.
A novel secure image steganography method based on chaos theory in spatial do...
This paper presents a novel approach of building a secure data hiding technique in digital images. The
image steganography technique takes the advantage of limited power of human visual system (HVS). It uses
image as cover media for embedding secret message. The most important requirement for a steganographic
algorithm is to be imperceptible while maximizing the size of the payload. In this paper a method is
proposed to encrypt the secret bits of the message based on chaos theory before embedding into the cover
image. A 3-3-2 LSB insertion method has been used for image steganography. Experimental results show a
substantial improvement in the Peak Signal to Noise Ratio (PSNR) and Image Fidelity (IF) value of the
proposed technique over the base technique of 3-3-2 LSB insertion.
sns
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- The algorithm takes an initial set of seed matches and refines them before adding them to a priority queue. It then repeatedly spawns new matches from the highest priority match and refines the spawns. Matches that satisfy criteria are added to the solution.
- The algorithm was evaluated on an object recognition benchmark and shown to significantly increase the discriminative power of a state-of-the-art local feature matching method.
A0360109
This document summarizes a research paper that proposes a new density-based clustering technique called Triangle-Density Based Clustering Technique (TDCT) to efficiently cluster large spatial datasets. TDCT uses a polygon approach where the number of data points inside each triangle of a polygon is calculated to determine triangle densities. Triangle densities are used to identify clusters based on a density confidence threshold. The technique aims to identify clusters of arbitrary shapes and densities while minimizing computational costs. Experimental results demonstrate the technique's superiority in terms of cluster quality and complexity compared to other density-based clustering algorithms.
Cristopher M. Bishop's tutorial on graphical models
Part 1 of the document provides an overview of graphical models and machine learning techniques for computer vision. It discusses directed and undirected graphs, inference using message passing algorithms like belief propagation, and learning techniques like maximum likelihood and Bayesian learning. Graphical models allow modeling complex distributions and exploiting conditional independencies between variables.
Synthetic aperture radar images
Computer apparition plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. The need for Radar imaging systems, to recover some sources that
are not within human visual band, is raised. This paper present new algorithm for Synthetic Aperture Radar (SAR) images segmentation based on thresholding technique. Entropy based image thresholding has
received sustainable interest in recent years. It is an important concept in the area of image processing.
Pal (1996) proposed a cross entropy thresholding method based on Gaussian distribution for bi-modal images. Our method is derived from Pal method that segment images using cross entropy thresholding based on Gamma distribution and can handle bi-modal and multimodal images. Our method is tested using
Synthetic Aperture Radar (SAR) images and it gave good results for bi-modal and multimodal images. The
results obtained are encouraging.
Multiple Style-Transfer in Real-Time
Style transfer aims to combine the content of one image with the artistic style of another. It was discovered that lower levels of convolutional networks captured style information, while higher levels captures content information. The original style transfer formulation used a weighted combination of VGG-16 layer activations to achieve this goal. Later, this was accomplished in real-time using a feed-forward network to learn the optimal combination of style and content features from the respective images. The first aim of our project was to introduce a framework for capturing the style from several images at once. We propose a method that extends the original real-time style transfer formulation by combining the features of several style images. This method successfully captures color information from the separate style images. The other aim of our project was to improve the temporal style continuity from frame to frame. Accordingly, we have experimented with the temporal stability of the output images and discussed the various available techniques that could be employed as alternatives.
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PPT steganography
This document discusses steganography, which is hiding messages within seemingly harmless carriers or covers so that no one apart from the intended recipient knows a message has been sent. It provides examples of steganography in text, images, and audio, as well as methods used for each. These include techniques like least significant bit insertion and temporal sampling rates. The document also covers steganalysis, which aims to detect hidden communications by analyzing changes in the statistical properties of covers.
Steganography and watermarking
This document provides an overview of steganography and watermarking techniques for hiding information in digital media. It defines steganography as "covered writing" involving hiding secret messages within other digital files like images, audio, or video. Common steganography methods embed data in the least significant bits of pixels or audio samples. Watermarking differs in embedding identifying marks that are robust to modifications and aim to protect copyrights. The document outlines various media and techniques for each, applications, advantages and limitations of both steganography and watermarking.
Steganography
Steganography is the art and science of hiding messages within other non-secret text, images, or other files. It works by encoding hidden messages within carrier files like images, videos, or documents in a way that avoids attracting attention to the message itself. Common techniques for steganography include least significant bit insertion and masking and filtering. While it can be used for privacy, it has also been used by hackers, terrorists, and criminals for illegal purposes.
An Image Steganography Algorithm Using Huffman and Interpixel Difference Enco...
Steganography is an art of hiding secret information on a cover medium through imperceptible methodology. The three pillars on which a steganography algorithm should be erected are: Embedding capacity, Imperceptibility and Robustness. It is fortunate that all these goals are interdependent on one another. The state of art is finding an optimum solution that keep up all the steganography goals. It is believed that there is no productivity if the size of cover medium gets extended to meet in housing the secret data on it. This happens due to lack in refinement of embedding algorithm and failing in analyzing the data structure of secret data. In this paper, an attempt has made to improve embedding capacity and bring very less distortion to the cover medium by analyzing the data structure of the payload. A residual coding is carried on the pay load before it is submitted to Huffman encoding which is a lossless compression technique. As a result, the representation of payload had shrink. Further, the variable bit encoding (Huffman) do a lossless compression and finally the payload get housed on the cover medium. This ended with high embedding capacity and less imperceptibility. Peak signal to noise ratio confirms that the residual coding had given improvised results than few existing embedding algorithm.
Steganography ProjectReport
This document discusses steganography techniques for hiding secret information in digital images. It begins with an introduction to steganography and its differences from cryptography. It then discusses various steganography techniques including least significant bit insertion, masking and filtering, and transform domain techniques. It also discusses using bitmap images for steganography and the popularity of formats like JPEG. The goal of the document is to provide an overview of digital image steganography techniques.
“Multimedia Steganography with Cipher Text and Compression ppt.
Multimedia Steganography with Cipher Text and Compression
This document discusses steganography techniques for hiding secret messages in various media formats. It describes how to embed messages in text, audio, images, and video files. For text, it explains using data compression like Huffman coding. Audio steganography slightly alters the binary sequence of sound files. Image steganography uses lossless compression to maintain image integrity while embedding data. Video steganography conceals any file within a carrier video. Statistical analysis can detect hidden messages by analyzing frequency distributions in covered files for abnormal patterns introduced during encoding.
Image Steganography
Presentation of Dissertation we had on Image Steganography in MCA Study.
Presenters: Vikas Kantiya and Hushen Savani
Steganography Project
This document provides an overview of steganography. It discusses how steganography hides messages within carriers so that the message is concealed. The document then discusses the history of steganography dating back to ancient Greece. It also discusses modern uses of steganography during the Cold War and by terrorist groups. The document outlines the objectives of the study which are to provide security during message transmission. It then discusses steganography techniques like the LSB algorithm and provides snapshots of its implementation. Finally, it discusses the results of using LSB steganography and concludes with possibilities for further enhancement.
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Similar to Steganography using reversible texture synthesis
Image Quilting in Steganography using Reversible Texture Formation
This document summarizes a research paper on using image quilting in steganography with reversible texture formation. The paper proposes a steganography method that hides data by synthesizing a texture image from a small source texture image. It uses image quilting to enhance image quality and generate arbitrary sized images by stitching together patches from the source image. This allows hiding large secret messages while maintaining high image quality. The proposed method is reversible, allowing extraction of the source texture and secret data without distortion. It offers improvements over existing steganography techniques by providing higher embedding capacity while preventing steganalysis attacks.
Image Enhancement and Restoration by Image Inpainting
Inpainting is the process of reconstructing lost or deteriorated part of images based on the background information. i. e .it fills the missing or damaged region in an image utilizing spatial information of its neighboring region. Inpainting algorithm have numerous applications. It is helpfully used for restoration of old films and object removal in digital photographs. The main goal of the algorithm is to modify the damaged region in an image in such a way that the inpainted region is undetectable to the ordinary observers who are not familiar with the original image. This proposed work presents image inpainting process for image enhancement and restoration by using structural, texture and exemplar techniques. This paper presents efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplar-based synthesis. Computational efficiency is achieved by a blockbased sampling process.
A Combined Model for Image Inpainting
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
I017265357
This document summarizes several existing techniques for image inpainting, including texture synthesis, geometric partial differential equations (PDEs), and coherence-based methods. It then proposes a combined model that incorporates elements of these different approaches into a variational framework. Specifically, it suggests combining texture synthesis, PDE-based diffusion, and enforcing coherence among neighboring pixels and across frames for video inpainting. The goal is to approximate the minimum of the proposed energy functional to better fill in missing or corrupted regions of images and video frames.
Av4102350358
The document summarizes an adaptive image steganography technique that embeds secret messages into digital images. It proposes using adaptive quantization embedding, where quantization steps for image blocks are optimized to guarantee more data can be embedded in busy image areas with high contrast. The technique embeds adaptive quantization parameters and message bits into the cover image using a difference expanding algorithm. Simulation results showed the proposed scheme can provide a good balance between imperceptibility and embedding capacity.
IRJET - Reversible Texture Combination Steganography
This document proposes a reversible texture combination steganography technique. It allows secret messages and the original cover image to be recovered after data extraction. The technique subdivides source images into patches, and generates composition images by combining patches. It embeds data into these synthesized texture images. An index table tracks the patch locations to allow recovery of the original cover image patches after data extraction. The technique uses authentication to securely embed and extract data while maintaining image quality and embedding capacity. It allows reuse of source textures for multiple rounds of steganography.
TEXTURE REPRESENTATION.pdf
Texture refers to the spatial arrangement of basic image elements or textons. It provides useful information for identifying objects or regions of interest. Texture representation has applications in areas like object recognition, medical imaging, and scene understanding. This document discusses several aspects of texture representation, including texture shape extraction, texture synthesis, and texture segmentation. It also describes challenges in representing textures from large images and video, and proposes a method to automatically infer a compact texture representation from input data without external information.
Hierarchical Gaussian Scale-Space on Androgenic Hair Pattern Recognition
Androgenic hair pattern stated to be the new biometric trait since 2014. The research to improve
the performance of androgenic hair pattern recognition system has begun to be developed due to the
problems that occurred when other apparent biometric trait such as face is hidden from sight. The
recognition system was built with hierarchical Gaussian scale-space using 4 octaves and 3 levels in each
octave. The system also implemented the equalization process to adjust image’s intensity by using
histogram equalization. We analyzed 400 images of androgenic hair in the database that were analyzed
using 2-fold and 10-fold cross validation and Euclidean distance to classify it. The experimental results
showed that our proposed method gave better performance compared to previous work that used Haar
wavelet transformation and principal component analysis as the main method. The best recognition
precision was 94.23 % obtained from the base octave with the third level using histogram equalization and
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K018137073
This document discusses using super-resolution-based in-painting for object removal in images. It begins with an overview of in-painting and exemplar-based in-painting methods. It then proposes a new framework that combines exemplar-based in-painting with a single-image super-resolution method. This approach improves image quality by producing high-resolution outputs with less noise compared to exemplar-based in-painting alone. The document concludes the proposed method increases robustness for applications like satellite imaging and medical imaging by providing high quality images with damaged objects removed.
A SECURE STEGANOGRAPHY APPROACH FOR CLOUD DATA USING ANN ALONG WITH PRIVATE K...
A SECURE STEGANOGRAPHY APPROACH FOR CLOUD DATA USING ANN ALONG WITH PRIVATE K...IJCSIS Research Publications
Cloud computing is a powerful, flexible, cost
efficient platform for providing consumer IT services
over the Internet. However Cloud Computing has
various level of risk because most important
information is maintained and managed by third party
vendors, which means harder to maintain security for
user’s data .Steganography is one of the ways to provide
security for secret data by inserting in an image or
video. In this most of the algorithms are based on the
Least Significant Bit (LSB), but the hackers easily
detects it embeds directly. An Efficient and secure
method of embedding secret message-extracting
message into or from color image using Artificial
Neural Network will be proposed. The proposed
method will be tested, implemented and analyzed for
various color images of different sizes and different
sizes of secret messages. The performance of the
algorithm will be analyzed by calculating various
parameters like PSNR, MSE and the results are good
compared to existing algorithms.Matching of large images through coupled decomposition
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Kentaro_region_filling_inpainting
This paper proposes a new algorithm for removing large objects from digital images by filling in the removed region in a visually plausible manner. It combines exemplar-based texture synthesis with structure propagation from image inpainting. The key insights are that exemplar-based texture synthesis can replicate both texture and structure if the filling order is determined by propagating confidence in pixel values similar to inpainting, and that a single algorithm can synthesize both pure and composite textures. The proposed algorithm efficiently synthesizes textures and propagates structures into the removed region by sampling exemplar patches based on a confidence term calculated from local image gradients. This allows it to generate natural-looking results while avoiding blurring issues of previous approaches.
Selective search
The document describes selective search, which combines the strengths of exhaustive search and segmentation. It generates candidate object locations by using image structure to guide the sampling process, rather than a blind search of all positions and scales. This yields a class-independent set of locations and is more efficient than exhaustive search, visiting fewer windows per image. Segmentation is also discussed, and the algorithm uses multiple grouping criteria and representations to generate diverse locations suited to different image conditions.
Image in Painting Techniques: A survey
This document provides a survey of different image inpainting techniques. It discusses approaches such as texture synthesis based inpainting, PDE (partial differential equation) based inpainting, exemplar based inpainting, hybrid inpainting, and semi-automatic inpainting. Texture synthesis approaches recreate textures within missing regions by sampling from surrounding textures. PDE based methods diffuse image information into missing areas. Exemplar based techniques iteratively copy patches from surrounding regions. Hybrid methods combine approaches. The document analyzes strengths and limitations of each technique.
Automatic Synthesis Of Isotropic Textures On Surfaces From Sample Images
This document summarizes a research paper on automatic texture synthesis on surfaces from sample images. It discusses:
1) A method for synthesizing isotropic textures on subdivision surfaces in a fully automatic way using Gaussian and Laplacian pyramids to construct coarse-to-fine texture synthesis that preserves first-order statistics.
2) Background on texture synthesis, including sample-based vs procedural methods, first and second order statistics, quilting methods, and neighborhood search methods.
3) Previous related work on texture synthesis methods by Efros and Leung using pixel-by-pixel growth, and by Wei and Levoy using multi-resolution neighborhood search.
Improved steganographic security by
In this paper, a new steganography algorithm has been suggested to enforce the security of data hiding and to increase the amount of payloads. This algorithm is based on four safety layers; the first safety layer has been initiated through compression and an encryption of a confidential message using a set partition in hierarchical trees (SPIHT) and advanced encryption standard (AES) mechanisms respectively. An irregular image segmentation algorithm (IIS) on a cover-image (Ic) has been constructed successfully in
the second safety layer, and it is based on the adaptive reallocation segments' edges (ARSE) by applying an
adaptive finite-element method (AFEM) to find the numerical solution of the proposed partial differential equation (PDE). An intelligent computing technique using a hybrid adaptive neural network with a modified ant colony optimizer (ANN_MACO) has been proposed in the third safety layer to construct a
learning system. This system accepts entry using support vector machine (SVM) to generate input patterns as features of byte attributes and produces new features to modify a cover-image. The significant innovation of the proposed novel steganography algorithm is applied efficiently on the forth
safety layer which is more robust for hiding a large amount of confidential message reach to six bits per pixel (bpp) into color images. The new approach of hiding algorithm works against statistical and visual attacks with high imperceptible of hiding data into stego-images (Is). The experimental results are
discussed and compared with the previous steganography algorithms; it demonstrates that the proposed algorithm has a significant improvement on the effect of the security level of steganography by making an arduous task of retrieving embedded confidential message from color images.
New and Unconventional Techniques in Pictorial Steganography and Steganalysis
1. The document discusses new and unconventional techniques in pictorial steganography and steganalysis. It introduces MPSteg-color, a new steganographic technique that hides messages in color image coefficients obtained through image decomposition, making the messages harder for steganalysts to detect.
2. The document also proposes a methodology for comparing different steganalysis techniques. An empirical evaluation of four steganalysis algorithms showed that their performance is highly dependent on the training and testing images used.
3. Two targeted steganalysis techniques designed to detect messages hidden using MPSteg-color are also introduced. One detects blocking artifacts introduced during embedding, while the other analyzes histograms of the decomposition
IMPROVED STEGANOGRAPHIC SECURITY BY APPLYING AN IRREGULAR IMAGE SEGMENTATION ...
In this paper, a new steganography algorithm has been suggested to enforce the security of data hiding and to increase the amount of payloads. This algorithm is based on four safety layers; the first safety layer has been initiated through compression and an encryption of a confidential message using a set partition in hierarchical trees (SPIHT) and advanced encryption standard (AES) mechanisms respectively. An irregular image segmentation algorithm (IIS) on a cover-image (Ic) has been constructed successfully in the second safety layer, and it is based on the adaptive reallocation segments' edges (ARSE) by applying an adaptive finite-element method (AFEM) to find the numerical solution of the proposed partial differential equation (PDE). An intelligent computing technique using a hybrid adaptive neural network with a modified ant colony optimizer (ANN_MACO) has been proposed in the third safety layer to construct a learning system. This system accepts entry using support vector machine (SVM) to generate input patterns as features of byte attributes and produces new features to modify a cover-image.
The significant innovation of the proposed novel steganography algorithm is applied efficiently on the forth safety layer which is more robust for hiding a large amount of confidential message reach to six bits per pixel (bpp) into color images. The new approach of hiding algorithm works against statistical and visual attacks with high imperceptible of hiding data into stego-images (Is). The experimental results are discussed and compared with the previous steganography algorithms; it demonstrates that the proposed algorithm has a significant improvement on the effect of the security level of steganography by making an arduous task of retrieving embedded confidential message from color images.
JPM1410 Images as Occlusions of Textures: A Framework for Segmentation
This document proposes a new mathematical and algorithmic framework for unsupervised image segmentation. It models images as occlusions of random textures, called textures, and shows that local histograms can segment such images. The framework draws on nonnegative matrix factorization and image deconvolution. Results on synthetic and real histology images show promise. Existing segmentation methods make assumptions that often fail on complex tissues, while proposed framework proves local histograms of texture-occluded images combine the textures' value distributions, allowing segmentation.
N42018588
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Hierarchical Gaussian Scale-Space on Androgenic Hair Pattern Recognition
Hierarchical Gaussian Scale-Space on Androgenic Hair Pattern Recognition
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Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
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Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
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Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
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Shakas Technologies ( Galaxy of Knowledge)
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Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
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Shakas Technologies ( Galaxy of Knowledge)
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Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
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Shakas Technologies ( Galaxy of Knowledge)
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Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
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Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
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Email : info@shakastech.com | shakastech@gmail.com |
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Shakas Technologies ( Galaxy of Knowledge)
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Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
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website: www.shakastech.com
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Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
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Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
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Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
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