AU2021107185A4 - Designing Imperceptible-High Payload Capacity Steganography Framework in Post Quantum Encrypted Domain using Deep Advanced Hierarchical Feature Learning. - Google Patents
Designing Imperceptible-High Payload Capacity Steganography Framework in Post Quantum Encrypted Domain using Deep Advanced Hierarchical Feature Learning. Download PDFInfo
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- 238000003058 natural language processing Methods 0.000 description 3
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
- H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
- H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
- H04N1/32149—Methods relating to embedding, encoding, decoding, detection or retrieval operations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
Abstract
Our invention Designing Imperceptible-High Payload Capacity Steganography Framework
in Post Quantum Encrypted Domain using Deep Advanced Hierarchical Feature Learning is
a persists a danger that intruders may get access to the sensitive information, causing it to
be shared, exposed, modified, and impacting its availability. Since fast multimedia
communication via different low-cost mediums such as smartphones and Social
Networking platforms may be readily exploited using an unsafe channel, steganography
has come to known as one of the most prominent fields of study. Steganography is a
technique of concealing a confidential small multimedia information within a much bigger
piece of multimedia information, which can be anything, be it an image, a video or a text
file. Technique of concealing one image within another image is known as Image
Steganography. The cover image in which the secret information needs to be hidden in
image steganography is altered in such a manner that the concealed information is not
evident, which makes it less dubious than cryptography. The embedding and extraction
algorithms are the two most important parts of any advanced steganographic framework.
The secret image, the secret key, and the cover image, which will be used to communicate
the message, are all inputs to the embedding method. The stego image is the result of the
embedding process. The stego image is also supplied as an input to the extraction process.
Steganalysis, on the other hand, is a technique used to identify the existence of any hidden
information in an image and to extract that information. Steganalysis aims in determining
if an image is a stego image or an original image.
TOTAL NO OF SHEET: 02 NO OF FIG: 04
101 102
Cover 00Cover
Media Media
Embeddi* StegO. Extraing
Atgerithm Oct Algerin
Secret __I104 106 Secret 107
Message Messge
103
Figure 1: Basic Steganography Framework
\200
Host Irnage CotIrr rnage
203
204 204
Secret Image ReeldImage
Encoding rarework Decoing Framework
Figure 2: A Basic Deep Steganography Framework
Plain tex (sender) Cipher text (receiver)
301 alg n publicchannel Drg ion 302
Fey
key
Quantum Que ryturn channel G r 30
Figure 3: Basic Post-Quantum Cryptography Framework
Description
TOTAL NO OF SHEET: 02 NO OF FIG: 04
101 102 Cover 00Cover Media Media
Embeddi* StegO. Extraing Atgerithm Oct Algerin Secret __I104 106 Secret 107 Message Messge 103
Figure 1: Basic Steganography Framework
\200
Host Irnage CotIrr rnage
203
204 204 Secret Image ReeldImage
Encoding rarework Decoing Framework
Figure 2: A Basic Deep Steganography Framework
Plain tex (sender) Cipher text (receiver)
301 alg n publicchannel Drg ion 302
key
Fey Quantum Que ryturn channel G r 30
Figure 3: Basic Post-Quantum Cryptography Framework
Australian Government IP Australia Innovation Patent Australia
Patent Title: Designing Imperceptible-High Payload Capacity Steganography Framework in Post Quantum Encrypted Domain using Deep Advanced Hierarchical Feature Learning.
Name and address of patentees(s): Kiran Malik, Research Scholar, Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women Kashmere Gate, Delhi - 110006 Dr. RAVINDER.M, Assistant Professor, Computer Science & Engineering, Indira Gandhi Delhi Technical University for Women, Madrasa Road, Opposite St. James Church, Kashmere Gate, New Delhi (India) Pin: 110006. Dr. Gaurav Indra, Assistant Professor, Department of Information Technology, Indira Gandhi Delhi Technical University For Women, Kashmere Gate, New Delhi (India) Pin: 110006. Complete Specification: Australian Government.
[500] Our Invention is related to a Designing Imperceptible-High Payload Capacity Steganography Framework in Post Quantum Encrypted Domain using Deep Advanced Hierarchical Feature Learning.
[502] Lately, cyber security concerns have made safeguarding the personal data over open networks a tough job. There persists a danger that intruders may get access to the sensitive information, causing it to be shared, exposed, modified, and impacting its availability. Since fast multimedia communication via different low-cost mediums such as smartphones and Social Networking platforms may be readily exploited using an unsafe channel, steganography has come to known as one of the most prominent fields of study.
[504] Steganography is a technique of concealing a confidential small multimedia information within a much bigger piece of multimedia information, which can be anything, be it an image, a video or a text file. Technique of concealing one image within another image is known as Image Steganography. The cover image in which the secret information needs to be hidden in image steganography is altered in such a manner that the concealed information is not evident, which makes it less dubious than cryptography.
[506] The embedding and extraction algorithms are the two most important parts of any advanced steganographic framework. The secret image, the secret key, and the cover image, which will be used to communicate the message, are all inputs to the embedding method. The stego image is the result of the embedding process.
[508] Steganalysis, on the other hand, is a technique used to identify the existence of any hidden information in an image and to extract that information. Steganalysis aims in determining if an image is a stego image or an original image.
[510] Thanks to the accessibility of large amounts of data. Deep Hierarchical Feature Learning is highly used for image processing and recognition, natural language processing, automatic voice recognition, among many other things.
[512] In comparison to standard steganography algorithms, the deep learning-based steganography technology can automatically hide and retrieve visual data. In the case of image steganography, no human intervention is required. By modifying parameter data, various data characteristics and the intensity of data embedding may be retrieved, considerably improving the effectiveness of image steganography.
[514] The challenge of building a more trustworthy and resilient steganographic system has become increasingly important as deep learning Steganalysis algorithms is developing rapidly.
[516] Post-quantum cryptography refers to cryptographic algorithms (usually public key algorithms) that are thought to be secure against a cryptanalytic attack by a quantum computer. As of 2021, this is not true for the most popular public-key algorithms, which can be efficiently broken by a sufficiently strong quantum computer.
[518] In recent years, there has been a substantial amount of research on quantum computers - machines that exploit quantum mechanical phenomena to solve mathematical problems that are difficult or intractable for conventional computers.
[520] If large-scale quantum computers are ever built, they will be able to break many of the public-key cryptosystems currently in use. This would seriously compromise the confidentiality and integrity of digital communications on the Internet and elsewhere.
[522] The goal of post-quantum cryptography is to develop cryptographic systems that are secure against both quantum and classical computers, and can interoperate with existing communications protocols and networks.
1. The objective of the invention is to provide a Designing Imperceptible-High Payload Capacity Steganography Framework in Post Quantum Encrypted Domain using Deep Advanced Hierarchical Feature Learning is a persists a danger that intruders may get access to the sensitive information, causing it to be shared, exposed, modified, and impacting its availability. 2. The other objective of the invention is to provide a fast multimedia communication via different low-cost mediums such as smartphones and Social Networking platforms may be readily exploited using an unsafe channel, steganography has come to known as one of the most prominent fields of study. Steganography is a technique of concealing a confidential small multimedia information within a much bigger piece of multimedia information, which can be anything, be it an image, a video or a text file. 3. The other objective of the invention is to provide a Technique of concealing one image within another image is known as Image Steganography. The cover image in which the secret information needs to be hidden in image steganography is altered in such a manner that the concealed information is not evident, which makes it less dubious than cryptography. The embedding and extraction algorithms are the two most important parts of any advanced steganographic framework. 4. The other objective of the invention is to provide a secret image, the secret key, and the cover image, which will be used to communicate the message, are all inputs to the embedding method. 5. The other objective of the invention is to provide a stego image is the result of the embedding process. The stego image is also supplied as an input to the extraction process. Steganalysis, on the other hand, is a technique used to identify the existence of any hidden information in an image and to extract that information.
6. The other objective of the invention is to provide a Deep Hierarchical Feature Learning is highly used for image processing and recognition, natural language processing, automatic voice recognition, among many other things. 7. The other objective of the invention is to provide a ensure an efficient transfer of confidential information through 8. The other objective of the invention is to provide a Steganography using post quantum cryptography module and deep learning frameworks with a challenge to keep payload capacity higher without sacrificing the imperceptibility and the security.
[524] A basic block diagram of my proposed work is given in the figure 1. The proposed scheme is divided into three stages
1. Pre-processing Stage feeds the secret image to a post quantum cryptography module to obtain an encrypted image. 2. Sender's framework witnesses the embedding of the encrypted image into the host image using a hiding network to produce a container image. This hiding network will be a deep hierarchical feature learning algorithm a method of steganography. 3. Receiver's framework takes the container image and through a revealed network, which is again a deep learning algorithm, produces a revealed image. The revealed image then goes through a decryption module to give the desired secret image.
[526] Five types of available approaches for image steganography utilising a GAN architecture are; A three network based GAN model, cycle-GAN based architectures, sender receiver architecture utilising GAN, coverless model where the cover image is produced randomly rather than being supplied as input, and an Alice, Bob, and Eve based model.
Lattice-based cryptography
[528] This approach includes cryptographic systems such as learning with errors, ring learning with errors (ring-LWE), the ring learning with errors key exchange and the ring learning with errors signature, the older NTRU or GGH encryption schemes, and the newer NTRU signature and BLISS signatures.
Multivariate cryptography
[530] This includes cryptographic systems such as the Rainbow (Unbalanced Oil and Vinegar) scheme which is based on the difficulty of solving systems of multivariate equations. Various attempts to build secure multivariate equation encryption schemes have failed. However, multivariate signature schemes like Rainbow could provide the basis for a quantum secure digital signature.
Hash-based cryptography
[532] This includes cryptographic systems such as Lamppost signatures and the Merkle signature scheme and the newer XMSS and SPHINCS schemes.
Code-based cryptography
[534] This includes cryptographic systems which rely on error-correcting codes, such as the Mc-Eliece and Niederreiter encryption algorithms and the related Courtois, Finiasz and Sendrier Signature scheme.
Figure 1: Basic Steganography Framework. Figure 2: A Basic Deep Steganography Framework. Figure 3: Basic Post-Quantum Cryptography Framework. Figure 4: Proposed Framework.
Material and Methodology
[536] This section provides the set of major SCI/SCIE/Scopus indexed journals, software tools and metrics accessible in this study area:
[538] List of Major SCI/SCIE/Scopus Indexed Journals in Image Steganography is
1. IEEE Transactions on Pattern Analysis and Machine Intelligence 2. IEEE Transactions on Image Processing 3. IEEE Transactions on Visualization and Computer Graphics iv) IEEE Transactions on Information Forensics and Security v) Multimedia Tools and Applications 4. International Journal of Computer Science and Security 5. Journal of Radio Engineering 6. Journal of Visual Communication and Image Representation
[540] TensorFlow, Thea no, Keras, Caffe, Torch, Deep Learning 4j, MxNet, CNTK, Lasagne, BigDL, and other frameworks are used to construct machine learning algorithms. Libraries in practice recently that I will be using to test stego-images are scikitlearn, Tensorflow, and Keras.
Scikit-learn
[542] The Scikit-learn framework is a free Python machine learning library that includes methods for classification, regression, and clustering. Scikit-learn is a Python interface that supports supervised and unsupervised learning methods. It is based on SciPy. The library focuses on data modelling. Scikit-learn contains a number of easy-to use clustering and decomposition techniques for unsupervised learning.
Tensor Flow
[544] Tensor Flow is an open framework that allows computing to be deployed across a variety of platforms, including CPUs, GPUs, and TPUs.
[546] The runtime library is cross-platform, and the C API isolates user-level code written in several languages from the core runtime. Over 200 common operations are included in the runtime library, including mathematics, array manipulation, control flow, and state management activities.
Keras
[548] Keras is a Python-based high-level neural network library that may be used with Tensor Flow, CNTK, or Theano. The main data structure is a layer organisation model. Keras includes neural network building blocks including layers, goals, activation functions, and optimizers that make working with picture and text data a lot simpler.
[550] Different metrics are used to assess various elements of picture steganography. Peak Signal to Noise Ratio, Correlation coefficient, histogram comparison, Structured Similarity Index Measure (SSIM), and Payload Capacity are some of the common metrics that I will be considering in my study.
Payload capacity
[552] It's a measurement of how much information is hidden behind the cover image. Bits per Pixel(BPP) is a typical representation for this. It is calculated as:
BPP = Number of Embedded secret bit
[554] In a steganographic system, payload capacity is significantly more important since the communication overhead is related to the maximum payload capacity.
Peak Signal to Noise Ratio (PSNR)
[556] Changes in the Cover image pixel values will occur since the cover image is updated to include the secret information. The alterations must be studied since they have a direct impact on the output Stego-image imperceptibility. PSNR is a common and high-quality statistic for evaluating the quality of a Stego-image by comparing the mean squared error value between the Cover and the Stegoimage. PSNR (in dB) = 10 logio
(2552) MSE Where, MSE = ZCn=1(In-In')2 C
[558] When comparing the stego and cover images, MSE is the mean squared error, C is the number of cover image pixels, In is the intensity of the ith pixel in the stego-image, and I'n is the intensity of the nth pixel in the cover image.
Correlation factor
[560] We calculate correlation factor _1 (correlation coefficient) r" as: 2
r= (ZCn=1(In-ptc)2) (Z Cn=1(In' -ps))
[562] Where C is the pixel count of the cover image, In'is the stego-image pixel intensity, and In is the cover image pixel intensity. The mean pixel values of the stego-image and cover image, respectively, are ys and c.
Methodology
[564] This section enlists the step-wise methodology that I will be following:
1. Firstly, a thorough review of the literature will be conducted to determine various techniques for Image Steganography that have been employed lately and in the past, as well as the mechanisms adopted by researchers to improve the efficiency of the same. 2. Following that, a target problem will be recognised from the research gaps and the challenges faced, leading to a specific problem formulation. 3. In addition, a suitable solution for the problem statement will be suggested. 4. Following that, the proposed solution will be deployed on an appropriate software platform and then will be tested and verified. 5. Ultimately, the suggested solution will be subjected to a qualitative and quantitative assessment.
WE CLAIMS 1. Our invention Designing Imperceptible-High Payload Capacity Steganography Framework in Post Quantum Encrypted Domain using Deep Advanced Hierarchical Feature Learning is a persists a danger that intruders may get access to the sensitive information, causing it to be shared, exposed, modified, and impacting its availability. Since fast multimedia communication via different low-cost mediums such as smartphones and Social Networking platforms may be readily exploited using an unsafe channel, steganography has come to known as one of the most prominent fields of study. Steganography is a technique of concealing a confidential small multimedia information within a much bigger piece of multimedia information, which can be anything, be it an image, a video or a text file. Technique of concealing one image within another image is known as Image Steganography. The cover image in which the secret information needs to be hidden in image steganography is altered in such a manner that the concealed information is not evident, which makes it less dubious than cryptography. The embedding and extraction algorithms are the two most important parts of any advanced steganographic framework. The secret image, the secret key, and the cover image, which will be used to communicate the message, are all inputs to the embedding method. The stego image is the result of the embedding process. The stego image is also supplied as an input to the extraction process. Steganalysis, on the other hand, is a technique used to identify the existence of any hidden information in an image and to extract that information. Steganalysis aims in determining if an image is a stego image or an original image. Deep learning (DL) has risen as a trend, and it is widely employed for a variety of applications, thanks to the accessibility of large amounts of data. Deep Hierarchical Feature Learning is highly used for image processing and recognition, natural language processing, automatic voice recognition, among many other things. 2. According to claims# the invention is to a Designing Imperceptible-High Payload Capacity Steganography Framework in Post Quantum Encrypted Domain using Deep Advanced Hierarchical Feature Learning is a persists a danger that intruders may get access to the sensitive information, causing it to be shared, exposed, modified, and impacting its availability. 3. According to claim,2# the invention is to a fast multimedia communication via different low-cost mediums such as smartphones and Social Networking platforms may be readily exploited using an unsafe channel, steganography has come to known as one of the most prominent fields of study. 4. According to claim,2,3# the invention is to a secret image, the secret key, and the cover image, which will be used to communicate the message, are all inputs to the embedding method and also the stego image is the result of the embedding process.
TOTAL NO OF SHEET: 02 NO OF FIG: 04 Aug 2021 2021107185
Figure 1: Basic Steganography Framework
Figure 2: A Basic Deep Steganography Framework
Figure 3: Basic Post-Quantum Cryptography Framework
TOTAL NO OF SHEET: 02 NO OF FIG: 04 Aug 2021 2021107185
Figure 4: Proposed Framework
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