Design and Development of Image Security Technique by Using Cryptography and Steganography: A Combine Approach

Saxena, Aumreesh Kumar; Sinha, Sitesh Kumar; Shukla, Piyush Kumar

doi:10.5815/ijigsp.2018.04.02

Cited by 24 publications

(3 citation statements)

References 8 publications

(10 reference statements)

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“…The characteristics of the classifiers should be extracted for machine learning classification. Functional vectors affect the classifiers' performance [ 27 ]. Two methods of extraction, character presence and character count, are generally used specifically.…”

Section: Algorithm For Tf-idfmentioning

confidence: 99%

Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

Sumathy¹,

Kumar

Sungeetha

et al. 2022

Computational Intelligence and Neuroscience

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The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user’s view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people’s decision-making. In recent years, emotions have been analyzed intensively in natural language, but many problems still have to be watched. One of the most important problems is the lack of precise classification resources. Most of the research into feeling gradation is concerned with the issue of polarity grading, although, in many practical applications, this relatively grounded feeling measure is insufficient. Design methods are therefore essential, which can accurately classify feelings into a natural language. The principal goal of the research is to develop an overflow of grammatical rules-based classification of Indian language tweets. In this work, three main challenges are identified to classify feelings in Indian language tweets and possible methods for tackling such issues. Firstly, it has been found that the informal nature of tweets is crucial for the classification of feelings. Based on the tweets, the mental illness of the person has been classified. Therefore, to categorize Indian language tweets, a combination of grammar rules based on adjectives and negations is proposed. Secondly, people often express their feelings with slang words, abbreviations, and mixed words. A technique called field tags is used to include nongrammatical arguments such as slang words and diverse words. Thirdly, if a tweet is more complex, the morphological richness of the Indian language results in a loss of performance. The grammar rules are embedded in N-gram techniques and machine learning methods. These methods are grouped into three approaches, which functionally predict Indian language tweets with syntactic words.

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Section: Algorithm For Tf-idfmentioning

confidence: 99%

Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

Sumathy¹,

Kumar

Sungeetha

et al. 2022

Computational Intelligence and Neuroscience

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“…steganography is achieving attraction by people caused by the security issues over the internet. Steganography has retreated a digital strategy of hiding a file in some form of multimedia, such as an image, an audio/video files (Saxena et al, 2018). The aim of steganography is hiding the embedded information in the cover image.…”

Section: Introductionmentioning

confidence: 99%

LSB-2 Steganography with Brotli Compression and base64 Encoding for Improving Data Embedding Capacity

Satriyawibawa,

Andono,

Soong

et al. 2024

SinkrOn

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Steganography functions as a technique for embedding messages or data in various forms of media, such as images, audio, video, or text, with the aim of avoiding detection by unauthorized parties. Steganography techniques can be used as a solution to hide and protect data. In this research, steganography will be carried out using images as the transmission object. This research was conducted to offer a modification of the Least Significant Bit (LSB) steganography technique using the LSB-2 method with Brotli compression and base64 encoding. Modification and use of Brotli compression and base64 coding aims to increase the message capacity that can be embedded in a transmission object while maintaining the quality of the transmission object. Experiments using small data and big data. The experimental results will be presented in tabular form by comparing the original image with the steganographically processed image using metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) as a comparison. The experiments carried out resulted in an increase in image capacity by reducing capacity usage with an average of 47.13% for small data and an average of 71.34%. The big data experiment resulted in an increase in the PSNR value of around 3.49%, accompanied by a decrease in the average MSE value of 33.85%, and a constant SSIM value of 0.9999, thus proving that the proposed method was successful in increasing image capacity and improving stego-image quality. when embedding big data.

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“…With the rapid development of image processing technology comes the growing need for the enhanced safety of image storage [1][2][3][4], especially in sensitive applications such as face recognition payment, ID verification, license plate recognition, satellite radar imaging and secret military image storage, electronic backup of confidential documents, etc. Cryptographic algorithms are often used in these applications as security assurance.…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

Design and Implementation of Real-Time Image Acquisition Chip Based on Triple-Hybrid Encryption System

Luo

Wang

et al. 2022

Electronics

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With the improved hardware storage capabilities and the rapid development of artificial intelligence image recognition technology, information is becoming image-oriented. Increasingly sensitive image data needs to be processed. When facing a large amount of real-time sensitive image data encryption and decryption, ensuring both the speed and the security is an urgent demand. This paper proposes an original triple-hybrid encryption system for a real-time sensitive image acquisition chip. This encryption system optimizes the symmetric encryption algorithm AES, asymmetric encryption algorithm ECC, and chip authentication algorithm PUF in pursuit of security, calculation speed, and to ensure that it is lightweight. The three optimized algorithms are further mixed and reused on the circuit level, to ensure mutual protection while making full use of their advantages. Apart from sensitive image protection at the algorithm level, the image chip itself is also protected by an innovative PUF chip authentication method that prevents it from being tampered with and copied. Triple-hybrid encryption system hardware implementation achieves a frequency of 132.5 MHz under the Virtex-5 FPGA with an area of 2834 Slices; with Virtex-7 FPGA, it reaches a frequency of 137.6 MHz with an area of 2716 Slices. The system is also implemented on SMIC 40 nm ASIC, and the clock frequency reaches 480 MHz and the area is 94,812.4 μm2. In terms of computing speed, the peak image encryption speed is 6.15 Gb/s, which meets the real-time image encryption requirement. In terms of hardware resource usage, AES reduced the hardware area by 60.1% compared with the results in other literature, ECC reduced the hardware area by 43.4%, and the PUF hardware area decreased exponentially with the increase in information entropy. The implementation of the three algorithms is reasonable and cost-effective, and the mixture of algorithms does not increase the required capacity of the hardware resource. The triple-hybrid encryption system cooperates with the image acquisition subsystem, storage subsystem, and asynchronous clock subsystem through software control to realize a complete triple-hybrid encryption SoC chip solution, and was successfully taped-out under the SMIC 40 nm process with all constraints passed and a total area of 10.59 mm2.

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Design and Development of Image Security Technique by Using Cryptography and Steganography: A Combine Approach

Cited by 24 publications

References 8 publications

Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

LSB-2 Steganography with Brotli Compression and base64 Encoding for Improving Data Embedding Capacity

Design and Implementation of Real-Time Image Acquisition Chip Based on Triple-Hybrid Encryption System

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