Concealing a Secret Message in a Colour Image Using an Electronic Workbench

Steganography is the art of concealing security data in media, such as pictures, audio, video, text, and protocols. The objective of this paper is hiding a secret message in a colour image to prevent an attacker from accessing the message. This is important because more people use the Internet all the time and network connections are spread around the world. The hidden secret message uses two general algorithms that are embedded and extracted. This paper proposes a new algorithm to conceal a secret message in a colour image in LSB. This algorithm includes three phases: 1) dividing the colour image into a number of blocks, 2) concealing the secret message, and 3) transmitting the stego-image from the sender in a multiplexer network and receiving it through a demultiplexer network using an electronic workbench. The outcome of the new algorithm demonstrates good efficiency, high security, and robustness and is executed quickly. The system is evaluated through the measurements of mean square error, peak signal-to-noise ratio, correlation, histogram, and capacity.

Improved Deep Hiding/Extraction Algorithm to Enhance the Payload Capacity and Security Level of Hidden Information

Steganography algorithms have become a significant technique for preventing illegal users from obtaining secret data. In this paper, a deep hiding/extraction algorithm has been improved (IDHEA) to hide a secret message in colour images. The proposed algorithm has been applied to enhance the payload capacity and reduce the time complexity. Modified LSB (MLSB) is based on disseminating secret data randomly on a cover-image and has been proposed to replace a number of bits per byte (Nbpb), up to 4 bits, to increase payload capacity and make it difficult to access the hiding data. The number of levels of the IDHEA algorithm has been specified randomly; each level uses a colour image, and from one level to the next, the image size is expanded, where this algorithm starts with a small size of a cover-image and increases the size of the image gradually or suddenly at the next level, according to an enlargement ratio. Lossless image compression based on the run-length encoding algorithm and Gzip has been applied to enable the size of the data that is hiding at the next level, and data encryption using the Advanced Encryption Standard algorithm (AES) has been introduced at each level to enhance the security level. Thus, the effectiveness of the proposed IDHEA algorithm has been measured at the last level, and the performance of the proposed hiding algorithm has been checked by many statistical and visual measures in terms of the embedding capacity and imperceptibility. Comparisons between the proposed approach and previous work have been implemented; it appears that the intended approach is better than the previously modified LSB algorithms, and it works against visual and statistical attacks with excellent performance achieved by using the detection error (PE). Furthermore, the results confirmed that the stego-image with high imperceptibility has reached even a payload capacity that is large and replaces twelve bits per pixel (12-bpp). Moreover, testing is confirmed in that the proposed algorithm can embed secret data efficiently with better visual quality.

‘I’m your automatic colour’ La colorazione automatica delle immagini in antropologia

Colour photographs now represent almost all the images produced with the new reality capture tools, mobile phones, which in 2020 ‘took’ 90% of all photos of that year. Black and white is relegated to artistic expression, even newspapers have converted to colour for some years. In the history of photography, although research on colour is attempted from the early stages, it is necessary to wait until 1861 with the experiences of James Clerk Maxwell who created a stable colour image. However, it is from the fifties of the twentieth century that the use of colour becomes ‘popular’ even in a more aesthetic dimension than an objective reproduction of reality. Part of the ethnographic, anthropological, archaeological and field research, on the other hand still makes use of consolidated and inexpensive black and white for a long time. On these images largely available online and open source you can conduct automatic colouring experiences. The procedure, managed with artificial intelligence algorithms with deep learning processes, is always more widely used with free applications and allows to obtain qualitatively more and more relevant results, even if some critical analysis is still necessary. This article presents the state of the art to 2021 of automatic colouring, with the comparison between algorithms developed since 2016 and showing with experimental examples both the possibilities of rendering and even the critical issues that emerged with the application in anthropological photographs, with the aim of extracting information that is not very evident in the originals in black and white.

Prediction of Soc in Calcic Chernozem in the Steppe Zone of Ukraine Using Brightness and Colour Indicators

Soil organic carbon (SOC) is an important component of any soil which determines many of its properties. Nowadays, more and more attention is being paid to the SOC content determination in soils by not using the conventional, time-consuming and expensive technique, but by using colour image processing of soil samples. In this case, even the camera of modern smartphones can be used as an image source, making this technique very convenient and practical. However, it is important to maintain certain standardised conditions (light intensity, light incidence angle, etc.) when capturing the images of soil samples. In our opinion, it is best to use a regular scanner for this purpose, with subsequent image processing by graphic programs (e.g., Adobe Photoshop). To increase the reliability of the colour information obtained in this way, it is desired (if possible) to use a spectrograph or a monochromator in the subsequent calculation of reflection or brightness ratios. It is these two approaches that we have implemented in our work. As a result of the experiment, the values of brightness ratios (at 480, 650 and 750 nm wavelengths and integral brightness ratio), colour indicators (the hue, saturation and value [HSV], red, green and blue [RGB], CIE L*a*b* and cyan, magenta, yellow and key [CMYK] systems) and SOC content in Calcic Chernozem samples of the steppe zone of Ukraine were obtained. Using correlation analysis of the dataset, the existence of direct (r = 0.88–0.90) and inverse close relationships (r = −0.75–0.90) between SOC, values of brightness ratios and colour indicators of the soil samples were established. This allows us to develop predictive models. Statistical analysis showed that the models were significant when they were based on the values of brightness ratios at 650 nm wavelength, integral brightness ratio, V indicator in HSV system, R, G and B indicators in RGB system, C, M and K indicators in CMYK system and L* and b* indicators in L*a*b* system. The subsequent calculation of variation coefficients showed that the largest variability was observed in SOC indicators (CV = 0.72) and slightly less variability in the K index of CMYK system and brightness ratio values at 650 nm wavelength (CV = 0.67 and 0.53, respectively). Based on this, we believe that the models y = 0.0188 + 0.0535*x (x is the value of the K index in CMYK system) and y = 5.0716 – 3.2255*log10(x) (x is the value of brightness ratio at 650 nm wavelength) were the most statistically significant and promising parameters for determining SOC content (y in these equations) in Calcic Chernozem samples of the steppe zone of Ukraine.

Intelligent High Payload Audio Watermarking Algorithm Using Colour Image in DWT-SVD Domain

Copyright protection and ownership verification of digital audio tracks have become increasingly important to be enabled by digital watermarking techniques. A novel high payload intelligent audio watermarking scheme with RGB color watermark image is proposed in this paper. The color watermark image is encrypted using Arnold chaotic map and passed through an adaptive scaling filter to scale the image to match the required payload. The encoding process is performed on the scaled encrypted version of the watermark image. A portion of the audio signal is used to embed a synchronization code and the other one is decomposed into short frames. These frames are processed with a two-level discrete wavelet transform (DWT), followed by a singular value decomposition (SVD) process on the approximation coefficients. The encoded watermark is inserted into the diagonal matrix using quantization index modulation (QIM). The inverse process of SVD and DWT is applied to obtain the marked audio signal. Blind extraction of the hidden information from the marked audio signal is performed in the reverse order of the embedding process. Experiments show that security, high payload, transparency and imperceptibility of the algorithm are satisfied. The robustness against several kinds of audio signal processing attacks is shown. Performance evaluation tests with SNR, BER, and FSIM are conducted.

The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics

This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for an ensemble learning algorithm. The Colour Feature Extreme Learning Machine (CF-ELM) is used in this study due to its additional ability to extract colour image features. The SCF-ELM is an ensemble learner that utilizes feature mapping via k-means clustering, a decision matrix and majority voting. It has been evaluated on a range of challenging agricultural object classification scenarios including weed, livestock and machinery detection. SCF-ELM model performance results were excellent both in terms of detection, 90 to 99% accuracy, and also inference times, around 0.01(s) per image. The SCF-ELM was able to compete or improve upon established algorithms in its class, indicating its potential for remote computing applications in agriculture.

Discrimination of the SARS–CoV-2 strains using of coloured s-LASCA-imaging of GB-speckles, developed for the gene “S” nucleotide sequences

Background: A recent bioinformatics technique involves changing nucleotide sequences into 2D speckles. This technique produces speckles called GB-speckles (Gene Based speckles). All classical strategies of speckle-optics, namely speckle-interferometry, subtraction of speckle-images as well as speckle-correlometry have been inferred for processing of GB-speckles. This indicates the considerable improvement in the present tools of bioinformatics. Methods: Colour s-LASCA imaging of virtual laser GB-speckles, a new method of high discrimination and typing of pathogenic viruses, has been developed. This method has been adapted to the detecting of natural mutations in nucleotide sequences, related to the spike glycoprotein (coding the gene «S») of SARS–CoV-2 gene as the molecular target. Results: The rate of the colouring images of virtual laser GB-speckles generated by s-LASCA can be described by the specific value of R. If the nucleotide sequences compared utilizing this approach the relevant images are completely identical, then the three components of the resulting colour image will be identical, and therefore the value of R will be equal to zero. However, if there are at least minimal differences in the matched nucleotide sequences, then the value of R will be positive. Conclusion: The high effectiveness of an application of the colour images of GB-speckles that were generated by s-LASCA- has been demonstrated for discrimination between different variants of the SARS–CoV-2 spike glycoprotein gene.

Image Enhancement for Colour Deficiency via Gamut Mapping

This paper describes a colour image enhancement method for those having colour-vision deficiencies. The proposed method can be divided into 3 stages. Firstly, a conversion relation between the wavelength shift (measured in nanometers) of a colour deficient observer (CDO) and the severity of colour deficiency was established. Secondly, the perceived colour gamut was built by applying the conversion relation. Finally, the original images were re-coloured by adopting a gamut mapping algorithm to map colours from the gamut of colour normal observer (CNO) to that of a CDO. Psychophysical experiments were then conducted to show the effectiveness of the method.