Comprehensive Survey of Image Steganography: Techniques, Evaluations, and Trends in Future Research

Book review: Applied cryptography: Protocols, algorithms, and source code in C

Peak signal to noise ratio (PSNR) and structural index similarity (SSIM) are two measuring tools that are widely used in image quality assessment. Especially in the steganography image, these two measuring instruments are used to measure the quality of imperceptibility. PSNR is used earlier than SSIM, is easy, has been widely used in various digital image measurements, and has been considered tested and valid. SSIM is a newer measurement tool that is designed based on three factors i.e. luminance, contrast, and structure to better suit the workings of the human visual system. Some research has discussed the correlation and comparison of these two measuring tools, but no research explicitly discusses and suggests which measurement tool is more suitable for steganography. This study aims to review, prove, and analyze the results of PSNR and SSIM measurements on three spatial domain image steganography methods, i.e. LSB, PVD, and CRT. Color images were chosen as container images because human vision is more sensitive to color changes than grayscale changes. Based on the test results found several opposing findings, where LSB has the most superior value based on PSNR and PVD get the most superior value based on SSIM. Additionally, the changes based on the histogram are more noticeable in LSB and CRT than in PVD. Other analyzes such as RS attack also show results that are more in line with SSIM measurements when compared to PSNR. Based on the results of testing and analysis, this research concludes that SSIM is a better measure of imperceptibility in all aspects and it is preferable that in the next steganographic research at least use SSIM.

PSNR vs SSIM: imperceptibility quality assessment for image steganography

Substitution boxes play an essential role in designing secure cryptosystems. With the evolution of quantum technologies, current data security mechanisms may be broken due to their construction based on mathematical computation. Quantum walks, a universal quantum computational model, play an essential role in designing quantum algorithms. We utilize the benefits of quantum walks to present a novel technique for constructing substitution boxes (S-boxes) based on quantum walks (QWs). The performance of the presented QWs S-box technique is evaluated by S-box evaluation criteria, and our results prove that the constructed S-box has vital qualities for viable applications in security purposes. Furthermore, a new technique for image steganography is constructed. The proposed technique is an integrated mechanism between classical data hiding and quantum walks to achieve better security for the embedded data. The embedding and extraction procedures are controlled by QWs S-box. The inclusion of cryptographic QWs S-box ensures the security of both embedding and extraction phases. At the extraction phase, only the stego image and the secret values are needed for constructing the secret data. Experimental results demonstrate that the presented technique has a high-security, high embedding capacity and good visual quality.

A novel image steganography technique based on quantum substitution boxes

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

Tomatoes at any given time has different maturity levels, therefore it is necessary to recognize the appropriate pattern to determine the level of maturity. One of the recognition patterns of tomatoes image is to use texture and color analysis. Texture analysis can be processed using the Gray Level Co-occurrence Matrix (GLCM) method. GLCM is chosen because it has a high degree of recognition based on the value of contrast, correlation, homogeneity, and energy. Furthermore, for color analysis one of the methods that can be used is Hue, Saturation, Value (HSV). By utilizing HSV, an object with a certain color can be detected and reduce the influence of the intensity of light from outside. The results of GLCM and HSV calculations can be classified to determined the maturity level of tomatoes by using K-Nearest Neighbour (K-NN) one of basic and simple classification method which utilizes the distance (k) as a comparison of the similarity level of the image. From the research we have done, using 100 data sets, consisting of 75 training data and 25 testing data that yields the highest accuracy rate of 100% with p value on GLCM is 9 and the membership value (k) in K-NN is 3. According to the experimental results, we can conclude that our proposed method can achieve the highest accuracy.

Tomatoes classification using K-NN based on GLCM and HSV color space

Rapid development of Internet makes transactions message even easier and faster. The main problem in the transactions message is security, especially if the message is private and secret. To secure these messages is usually done with steganography or cryptography. Steganography is a way to hide messages into other digital content such as images, video or audio so it does not seem nondescript from the outside. While cryptography is a technique to encrypt messages so that messages can not be read directly. In this paper have proposed combination of steganography using discrete cosine transform (DCT) and cryptography using the one-time pad or vernam cipher implemented on a digital image. The measurement method used to determine the quality of stego image is the peak signal to noise ratio (PSNR) and ormalize cross Correlation (NCC) to measure the quality of the extraction of the decrypted message. Of steganography and encryption methods proposed obtained satisfactory results with PSNR and NCC high and resistant to JPEG compression and median filter. Keywords â€”Image Steganography, Discrete Cosine Transform (DCT), One Time Pad, Vernam, Chiper, Image Cryptography

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Secure Image Steganography Algorithm Based on DCT with OTP Encryption

Face recognition that is technology used for recognizing human faces based on certain patterns and re-detect faces in various conditions. Face recognition is currently becoming popular to be applied in various ways, especially in security systems. Various methods of face recognition have been proposed in researches and increased accuracy is the main goal in the development of face recognition methods. FaceNet is one of the new methods in face recognition technology. This method is based on a deep convolutional network and triplet loss training to carry out training data, but the training process requires complex computing and a long time. By integrating the Tensorflow learning machine and pre-trained model, the training time needed is much shorter. This research aims to conduct surveys, test performance, and compare the accuracy of the results of recognizing the face of the FaceNet method with various other methods that have been developed previously. Implementation of the FaceNet method in research using two types of pre-trained models, namely CASIA-WebFace and VGGFace2, and tested on various data sets of standard face images that have been widely used before. From the results of this research experiment, FaceNet showed excellent results and was superior to other methods. By using VGGFace2 pre-trained models, FaceNet is able to touch 100% accuracy on YALE, JAFFE, AT & T datasets, Essex faces95, Essex grimace, 99.375% for Essex faces94 dataset and the worst 77.67% for the faces96 dataset.

Face Recognition using FaceNet (Survey, Performance Test, and Comparison)

Securing images can be achieved using cryptography and steganography. Combining both techniques can improve the security of images. Usually, Arnoldâ€™s transformation (ACM) is used to encrypt an image by randomizing the image pixels. However, applying only a transformation algorithm is not secure enough to protect the image. In this study, ACM was combined with RSA, another encryption technique, which has an exponential process that uses large numbers. This can confuse attackers when they try to decrypt the cipher images. Furthermore, this paper also proposes combing ACM with RSA and subsequently embedding the result in a cover image with inverted two-bit LSB steganography, which replaces two bits in the bit plane of the cover image with message bits. This modified steganography technique can provide twice the capacity of the previous method. The experimental result was evaluated using PSNR and entropy as the parameters to obtain the quality of the stego images and the cipher images. The proposed method produced a highest PSNR of 57.8493 dB and entropy equal to 7.9948.

A Combination of Inverted LSB, RSA, and Arnold Transformation to get Secure and Imperceptible Image Steganography

Cow is one of the animals that have many benefits for humans. There are various types of cows based on benefits such as dairy cows, beef cattle, worker cattle, and others. Cattle breeding should be tailored to the needs of the public. Less knowledge about different types of cattle can reduce the benefits of farmed cattle. Content Based Image Retrieval (CBIR) can be applied to help the problem of distinguishing or knowing the type of cow. The first step of the method proposed in this research is preprocessing by changing the background color, resizing and conversion of color space. Color feature extraction calculates the average and standard deviation of the color intensity of each color component. Next extract the texture feature using Gray Level Cooccurrence Matrix (GLCM) to look for contrast, energy, correlation, homogeneity and entropy at each angle 0°, 45°, 90° and 135° with a mean of 1 averaged. Six color features and five texture features are used as attributes to perform calculations with Euclidean Distance, so it can be known the similarity between images. Cattle types used include Limousin, Simental, Brangus, Peranakan Ongole (PO), and Frisien Holstein (FH). With 100 training images and 20 test images. To measure the accuracy of the proposed CBIR is used Confusion Matrix. Based on the measurement results obtained accuracy of 95% while the precision and recall obtained 100%.

Christy Atika Sari

Papers

Tomatoes classification using K-NN based on GLCM and HSV color space

Secure Image Steganography Algorithm Based on DCT with OTP Encryption

Face Recognition using FaceNet (Survey, Performance Test, and Comparison)

A Combination of Inverted LSB, RSA, and Arnold Transformation to get Secure and Imperceptible Image Steganography

CBIR for classification of cow types using GLCM and color features extraction