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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.

One of the most dangerous malignancies in global diseases is skin cancer. The best way to survive skin cancer is to detect it in its early stages. Its diagnosis is made by medical imaging with dermoscopy, which provides High-Resolution (HR) lesion images to simplify the detection process. Dermoscopy is not available to everyone, and other available imaging technologies, such as mobile devices, deteriorate the image quality required for diagnosis. This research presents a proposed novel Enhanced Deep Super-Resolution Generative Adversarial Network (EDSR-GAN) model to generate HR skin disease images from Low-Resolution (LR) ones. Particularly, a new design of the loss function is adopted for more details and for creating HR images. Experimental results reveal that the proposed model provides a higher performance on the HAM10000 dataset compared with other models applied on the same dataset. Additionally, for evaluating the proposed model, a variety of metrics are employed, including the Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index (SSIM), Multi-scale Structural Similarity Index (MS-SSIM), and histogram characteristics, to conduct thorough and impartial comparisons and identify the best models according to performance training time, and storage space. By obtaining an accuracy of 98.9958%, the results show that the proposed model outperforms traditional and previous models for color and texture reconstruction and recognition.

Improved software for processing medical images has inspired tremendous interest in modern medicine in recent years. Modern healthcare equipment generates huge amounts of data, such as scanned medical images and computerized patient information, which must be secured for future use. Diversity in the healthcare industry, namely in the form of medical data, is one of the largest challenges for researchers. Cloud environment and the Block chain technology have both demonstrated their own use. The purpose of this study is to combine both technologies for safe and secure transaction. Storing or sending medical data through public clouds exposes information into potential eavesdropping, data breaches and unauthorized access. Encrypting data before transmission is crucial to mitigate these security risks. As a result, a Blockchain based Chaotic Arnold’s cat map Encryption Scheme (BCAES) is proposed in this paper. The BCAES first encrypts the image using Arnold’s cat map encryption scheme and then sends the encrypted image into Cloud Server and stores the signed document of plain image into blockchain. As blockchain is often considered more secure due to its distributed nature and consensus mechanism, data receiver will ensure data integrity and authenticity of image after decryption using signed document stored into the blockchain. Various analysis techniques have been used to examine the proposed scheme. The results of analysis like key sensitivity analysis, key space analysis, Information Entropy, histogram correlation of adjacent pixels, Number of Pixel Change Rate, Peak Signal Noise Ratio, Unified Average Changing Intensity, and similarity analysis like Mean Square Error, and Structural Similarity Index Measure illustrated that our proposed scheme is an efficient encryption scheme as compared to some recent literature. Our current achievements surpass all previous endeavors, setting a new standard of excellence.

Generative Adversarial Network (GAN) is one of the recent developments in the area of deep learning to transform the images from one domain to another domain. While transforming the images, we need to make sure that the background information should not influence the learning process. The attention-based networks are developed to learn the saliency maps and to prioritize the learning based on the important image regions. We develop a new Cyclic Synthesized Attention Generative Adversarial Network (CSA-GAN) in this paper by incorporating the cycle synthesized loss with the attention network. The use of attention guidance as well as cycle synthesis objective reduces the learning space more towards the optimum solution. It also improves the rate of convergence. The proposed method is tested for Sketch to Face synthesis over CUHK and AR benchmark datasets. We also experimented for thermal to visible face synthesis over WHU-IIP dataset. The proposed CSA-GAN observed promising performance for face synthesis in comparison with state-of-the-art GAN methods.

The incidence of melanoma, the most serious form of skin cancer, has been increasing rapidly in recent years. Early diagnosis is crucial for successful treatment. Dermoscopy, a reliable medical technique, utilizes specialized devices to examine the skin and detect melanoma. With advancements in digital imaging, high-quality images of these examinations can now be captured and stored. These images are being standardized and used for automated melanoma detection. However, the presence of hair on the skin poses a challenge to accurate diagnosis. Thus, it is essential to remove hair to obtain precise results. In this paper, we propose a simple yet effective method for hair removal using deep learning. Our approach leverages the architecture of generative adversarial networks (GAN) combined with convolutional neural networks (CNN) to reconstruct hair-free images. The GAN consists of a generator and a discriminator. The generator takes a dermoscopy image as input and aims to generate a latent distribution that eliminates hair, considering it as noise. Simultaneously, the discriminator detects changes in the generated image. This iterative process continues until the discriminator fails to identify any changes, considering the generated image as the original hairless image. To evaluate our proposed model, a dataset comprising both hair-covered and hairless images is required. As such a dataset does not currently exist, we introduce a new dataset called Modified-HAM10000 (M-HAM10000), inspired by the scientifically curated dermoscopy dataset HAM10000. Experimental results demonstrate the improved performance of our technique on the M-HAM10000 dataset. Furthermore, we employ various evaluation metrics including Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Multiscale Structural Similarity Index (MS-SSIM) to assess our model's effectiveness. Through experiments conducted on the publicly available M-HAM10000 dataset, our proposed method demonstrates high efficiency in hair removal, enhancing the accuracy of skin disease diagnostics compared to other existing methods.

As a possible implementation of a low-power wide-area network (LPWAN), Long Range (LoRa) technology is considered to be the future wireless communication standard for the Internet of Things (IoT) as it offers competitive features, such as a long communication range, low cost, and reduced power consumption, which make it an optimum alternative to the current wireless sensor networks and conventional cellular technologies. However, the limited bandwidth available for physical layer modulation in LoRa makes it unsuitable for high bit rate data transfer from devices like image sensors. In this paper, we propose a new method for mangrove forest monitoring in Malaysia, wherein we transfer image sensor data over the LoRa physical layer (PHY) in a node-to-node network model. In implementing this method, we produce a novel scheme for overcoming the bandwidth limitation of LoRa. With this scheme the images, which requires high data rate to transfer, collected by the sensor are encrypted as hexadecimal data and then split into packets for transfer via the LoRa physical layer (PHY). To assess the quality of images transferred using this scheme, we measured the packet loss rate, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) index of each image. These measurements verify the proposed scheme for image transmission, and support the industrial and academic trend which promotes LoRa as the future solution for IoT infrastructure.

With the continuous advancement of deep learning technology, video forgery technology brings serious negative social impacts. However, existing video forgery detection technologies suffer from low detection accuracy, poor feature extraction capabilities, and insufficient robustness. Therefore, the study proposes two video forgery detection models based on Improved Efficient-Net and multi-modal feature fusion. The Improved Efficient-Net model utilizes structural similarity coefficients to enhance the video images and introduces a hybrid attention module in the Efficient-Net. The multi-modal feature fusion model uses the red, green, and blue domains of the image, the frequency domain, and the optical flow field features for fusion, and uses a hybrid loss function to weight all the loss function errors. The experiment shows that the maximum recognition accuracy of the improved Efficient-Net in the FaceForensics++ dataset is 98.57%, which is 6.24% as well as 9.53% higher than the baseline Efficient-Net and Convolutional Visual Transformer models, respectively. In the FaceForensics++ dataset, the multi-modal feature fusion model is able to achieve a recognition accuracy of 99.26%. In the BioDeepAV dataset, the multi-modal feature fusion model has a maximum decrease in recognition accuracy of 20.57%, which is 2.81% less than the benchmark Efficient-Net model, and the recognition accuracy is still the highest among all models. Therefore, the improved model can validly improve the accuracy of forged video identification, improve the efficiency of Internet supervision, and reduce the social harm of video forgery.

Precision agriculture is the mechanism which controls the land productivity and maximizes the revinue and minimizes the impact on sorroundings by automating the complete agriculture processes. This projected work relies on independent internet of things (IoT) enabled wireless sensor network (WSN) framework consisting of soil moisture (MC) probe, soil temperature measuring device, environmental temperature sensor, environmental humidity sensing device, CO 2 sensor, daylight intensity device (light dependent resistor) to acquire real-time farm information through multi-point measurement. The projected observance technique consists of all standalone IoT-enabled WSN nodes used for timely data acquisitions and storage of agriculture information. The farm history is additionally stored for generating necessary action throughout the whole course of farming. The work summarizes the optimum usage of irrigation by the precise management of water valve using neural network-based prediction of soil water requirement in 1 h ahead. Our proposed irrigation control scheme utilizes structural similarity (SSIM)-based water valve management mechanism which is used to locate farm regions having water deficiency. Moreover, a close comparative study of optimization techniques, like variable learning rate gradient descent, gradient descent for feedforward neural network-based pattern classification, is performed and the best practice is employed to forecast soil MC on hourly basis together with interpolation method for generating soil moisture content (MC) distribution map. Finally, SSIM index-based soil MC deficiency is calculated to manipulate the specified valves for maintaining uniform water requirement through the entire farm area. The valve control commands are again processed using fuzzy logic-based weather condition modeling system to manipulate control commands by considering different weather conditions.