Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

In this paper, a novel robust color image watermarking method based on Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed. In this method, RGB cover image is divided into red, green and blue components. DCT and DWT are applied to each color components. Grayscale watermark image is scrambled by using Arnold transform. DCT is performed to the scrambled watermark image. Transformed watermark image is then divided into equal smaller parts. DCT coefficients of each watermark parts are embedded into four DWT bands of the color components of the cover image. The robustness of the proposed color image watermarking has been demonstrated by applying various image processing operations such as rotating, resizing, filtering, jpeg compression, and noise adding to the watermarked images. Experimental results show that the proposed method is robust to the linear and nonlinear attacks and the transparency of the watermarked images has been protected.

A novel hybrid DCT and DWT based robust watermarking algorithm for color images

Image Steganography is the process of hiding information which can be text, image or video inside a cover image. The secret information is hidden in a way that it not visible to the human eyes. Deep learning technology, which has emerged as a powerful tool in various applications including image steganography, has received increased attention recently. The main goal of this paper is to explore and discuss various deep learning methods available in image steganography field. Deep learning techniques used for image steganography can be broadly divided into three categories - traditional methods, Convolutional Neural Network-based and General Adversarial Network-based methods. Along with the methodology, an elaborate summary on the datasets used, experimental set-ups considered and the evaluation metrics commonly used are described in this paper. A table summarizing all the details are also provided for easy reference. This paper aims to help the fellow researchers by compiling the current trends, challenges and some future direction in this field.

https://northumbria-test.eprints-hosting.org/id/document/273545

Image Steganography: A Review of the Recent Advances

Internet of Things (IoT) is a domain where the transfer of big data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, a highly secured technique is proposed using IoT protocol and steganography. This work proposes an image steganography procedure by utilizing the combination of various algorithms that build the security of the secret data by utilizing Binary bit-plane decomposition (BBPD) based image encryption technique. Thereafter a Salp Swarm Optimization Algorithm (SSOA) based adaptive embedding process is proposed to increase the payload capacity by setting different parameters in the steganographic embedding function for edge and smooth blocks. Here the SSOA algorithm is used to localize the edge and smooth blocks efficiently. Then, the hybrid Fuzzy Neural Network with a backpropagation learning algorithm is used to enhance the quality of the stego images. Then these stego images are transferred to the destination in the highly secured protocol of IoT. The proposed steganography technique shows better results in terms of security, image quality, and payload capacity in comparison with the existing state of art methods.

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SSII: Secured and High-Quality Steganography Using Intelligent Hybrid Optimization Algorithms for IoT

These days, researchers have used many techniques in e-multimedia data for intellectual property protection. One of these important techniques is watermarking which is superior to Digital Signature because it does not rise overhead and it can prevent unauthorized users from misusing digital information. However, the number of attacks with aim of distorting watermark signal or disturbing watermarks detecting are exploding. Consequently, the watermark techniques have became more complicated, but still they are not fully robust against some geometric distortions. To overcome this issue and to be enough robust against the malicious attacks, and provide a high level of imperceptibility,the watermark schemes have discovered and developed the advantages by means of Singular-value decomposition. Therefore, in recent decades there has been a significant increasing in using of SVD domain in many new image watermarking schemes, which show high robustness and imperceptibility. This discussion of SVD in image watermarking schemes included: a concise overview on watermarking systems, a brief review on SVD properties in image processing and its weaknesses, the concept of SVD components, the main classification of different SVD based image-watermarking schemes; a brief summary of various secure SVD-based watermarking techniques and all major SVD coefficients modifying strategies are presented. In addition, potential issues and some existing solutions and guidelines are provided. Furthermore, the performance comparisons of the discussed techniques are presented in tabular format. This article contribution provides essential and potential information for the researchers to design, develop and implement efficient robust and hybrid SVD-based image watermarking systems.

Robust and hybrid SVD-based image watermarking schemes:

A high security data hiding approach using image cropping and Least Significant Bit (LSB) steganography is proposed. The predefined certain secret coordinate crops are extracted from the cover image. The secret text message is divided into parts with the same image crops. Each part of the secret text message is embedded into an image crop with secret sequence using LSB approach. The embedding is done using the cover image of three color channels. The stego image is given by reassembling the image and the stego crops. A detailed comparative study is performed between the proposed approach and the other state-of-the-art approaches. This comparison is based on visualization to detect any degradation in stego image, difficulty of extracting the embedded data by any unauthorized viewer, Peak Signal-to-Noise Ratio (PSNR) of stego image, and the embedding algorithm CPU time. Experimental results shows that the proposed approach is more secure compared with the other traditional approaches.

High security data hiding using image cropping and LSB least significant bit steganography

Digital watermarking is an efficient and promising mechanism for protecting the copyright of the transmitted multimedia information. Thus, this paper presents two robust hybrid color image watermarking techniques. The objective of the proposed watermarking techniques is to increase the immunity of the watermarked color images against attacks and to achieve adequate perceptual quality. The first proposed hybrid technique is the homomorphic transform based Singular Value Decomposition (SVD) in Discrete Wavelet Transform (DWT) domain. Firstly, the DWT is employed to divide an image into non-overlapping bands. Then, the reflectance components of the LL sub-bands are extracted using the homomorphic transform of each of the RGB (Red, Green, and Blue) color image components. After that, the watermark is embedded by applying the SVD on these reflectance components. The second proposed hybrid technique is the three-level Discrete Stationary Wavelet Transform (DSWT) in Discrete Cosine Transform (DCT) domain. In this technique, the RGB components of the host color image are separated, and then the DCT is applied on each separated color component. The three-level DSWT is employed to divide the DCT components into four sub-bands. These sub-bands are the A, H, V, and D matrices, which have the same host image size. The watermark image is then embedded into the determined matrix A. The two proposed hybrid watermarking techniques are compared with the current state-of-the-art techniques. This paper also presents a comparative study of the proposed techniques for different color imaging systems to determine their robustness and stability. The comparisons are based on the subjective visual results to detect any degradation in the watermarked image in addition to the objective results of the Peak Signal-to-Noise Ratio (PSNR) of the watermarked image, and the Normalized Correlation (NC) of the extracted watermark to test and evaluate the performance efficiency of the proposed watermarking techniques. Extensive experimental results show that the proposed hybrid watermarking techniques are both robust and have adequate immunity against different types of attacks compared to the traditional watermarking techniques. They achieve not only very good perceptual quality with appreciated PSNR values, but also high correlation coefficient values in the presence of different multimedia attacks.

Robust hybrid watermarking techniques for different color imaging systems

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A Comparative Study For Color Systems Used In The DCT-DWT Watermarking Algorithm

This paper proposes a hybrid robust image watermarking scheme based on three levels of Discrete Stationary Wavelet Transform (DSWT) in the Discrete Cosine Transform (DCT) domain. The host image colors (red, green and blue) are separated, and then the DCT is applied on each color after separation. The DSWT is utilized to divide the DCT output into four sub-bands (3 levels). These sub-bands are (A, H, V, D) matrices with the same image size. The watermark is embedded on matrix A. The results of the proposed watermarking scheme are compared with other state-of-the-art schemes. The comparison is based on visualization to detect any degradation of the watermarked image, Peak Signal-to-Noise Ratio (PSNR) of the watermarked image, Normal Correlation (NC) of the extracted watermark after detection, applying attacks, and then calculating the PSNR and NC.

A hybrid scheme for robust color image watermarking using DSWT in DCT domain

This paper proposes a robust image watermarking approach using homomorphic based SVD in the DWT domain. The DWT is utilized to divide the host image into non-overlapping bands. The reflectance component of sub-band LL is extracted using homomorphic transform for each color (red, green and blue). The embedding watermark is done by applying SVD on the reflectance component of the sub-band LL. The results of the proposed watermarking approach are compared with those of the other traditional approaches. Evaluation is based on visualization, Peak Signal-to-Noise Ratio of watermarked image (PSNR), Normalized Correlation of watermark after detection (NC). Experimental results ensure that the proposed watermarking approach is both robust and immune to attacks studied in this paper compared with the other traditional approaches.

Khalid A. Al-Afandy

Papers

High security data hiding using image cropping and LSB least significant bit steganography

Robust hybrid watermarking techniques for different color imaging systems

A Comparative Study For Color Systems Used In The DCT-DWT Watermarking Algorithm

A hybrid scheme for robust color image watermarking using DSWT in DCT domain

Efficient color image watermarking using homomorphic based SVD in DWT domain