[기술해설]암호법(Cryptography)

Water quality is crucial for maintaining a sustainable living environment in aquaculture. Limnological parameters affects the fish physiology, growth rate, and feed efficiency and may lead to high mortality rate under extreme conditions. The development of an adaptive aquaculture monitoring system for water quality using fuzzy logic will address this problem. Using Mamdani-type fuzzy inferences system (FIS) model, the input limnological parameters such as pH, temperature, total dissolved solids, and dissolved oxygen levels were transformed to four output states: excellent, good, poor, and toxic, for the prediction of water quality. For the simulation and evaluation of the developed FIS, MATLAB Simulink was used. Results of this study can be integrated with a feedback system for appropriate treatments including filtering, aeration, and water flushing to maintain safe environment for Nile tilapia.

Fuzzy Logic-Based Adaptive Aquaculture Water Monitoring System Based on Instantaneous Limnological Parameters

Steganography is the method of hiding messages or data (plain text) by encoding them into some other data (usually images). Through this mechanism, the secured plain text message can be completely concealed from unauthorized access. Using steganography, we can hide messages by encoding it in an image and then send it to the receiving user. In our proposed method using steganography and encryption, we have implemented a mechanism to transmit a message from the sender to the receiver and increase the security of the transmission while also providing confidentiality. The receiver will receive both the encoded image and the key with why the encrypted method can be decrypted. We will be using the Least Significant Bit (LSB) method of steganography and AES encryption to achieve this.

Implementation of Least Significant Bit Image Steganography with Advanced Encryption Standard

While traditional embedding methods which are usually based on LSBs provide security to hidden messages in image-based Steganography, steganalyses have shown these methods to be vulnerable to detection and extraction, thus compromising the security of embedded messages. In this study, a new embedding method based on LSB is introduced to address this vulnerability. By using a one-time pad encryption method, the security of secret messages before embedding is ensured. A partitioning method based on columnar transposition and RGB color plane scattering technique is employed to improve scattering and ensure randomization during embedding. The experimental result shows an excellent outcome of the Peak-signal-to-noise Ratio (PSNR), Mean square Error (MSE), and Structural Similarity (SSIM) index. The PSNR value of the 5 test images results ranges from 53.2122 to 60.8716 all are higher than the acceptable value of PSNR, which is 30db.The average MSE result is0.2766 for all color planes, which also provide an outstanding result since the lower the MSE means the accumulative squared error between the stego image and the original one is lower. The average SSIM obtained in the testing is 0.9792for the 5 test images, which is very close to 1. The result supports the claim that the resulting stego images have low distortion which means that the cover image is almost the same with the stego image and that the modified LSB embedding algorithm provides excellent image quality with high imperceptibility and provides added security layers.

A Modified Least Significant Bit Randomized Embedding Method based on Image Partitioning and Columnar Transposition with Encryption

Strengthening the field of medical informatics has become extremely important and challenging in today’s era of digital image transmission. The present work discusses a novel image steganography method that can withstand either statistical tool-based or machine learning (ML)-based steganalysis attacks to ensure secure transmission of the medical images like chest X-rays, CT or MRI reports to the medical experts or hospitals for early diagnosis and fast recovery of the infected persons. The Rivest–Shamir–Adalmen (RSA) encryption technique is incorporated to convert a secret medical image into a cipher image which is enveloped inside a unique fractal cover thus enhancing robustness of the proposed method without debasing perceptual quality of the image. A 1-least significant bit (LSB), 2-LSB and 4-LSB spatial domain method is implemented and compared for hiding the encrypted secret medical image inside a fractal cover image which adds another layer of security. The novelty of the proposed algorithm is the use of fractal cover image with zero skewness angle to affirm the secure location for medical image hiding. Furthermore, steganalysis checks are performed using a deep learning feature extraction model ‘Xception’ and ‘WEKA’ image classifiers which authenticate security and integrity of the patients` confidential data. Stego performance of the proposed method is also evaluated by steganalysis tools such as StegSecret, StegExpose and histogram analysis. The statistical and differential analysis performed demonstrates the efficiency of the proposed algorithm over the existing techniques. 

Securing the COVID Patients’ Medical Records Using Encrypted Image Steganography

Various researches in image steganography focusing on spatial domain specifically in the least Significant Bit (LSB) embedding method had been conducted to improve the embedding capacity while maintaining high imperceptibility. However, these improvements were countered by various statistical attacks. This paper presents an LSB-based image steganography technique that uses YCbCr color space for the embedding process. Also, a new cover selection method to strengthen the proposed embedding algorithm was introduced in this paper. The cover selection mechanism used the skewness and kurtosis of the candidate cover images as factors to determine if a candidate cover image will yield to a high probability of detection or not. The distortions analysis affirms that the stego-images produced by the embedding method obtained acceptable PSNRs and SSIMs, thus, proving that the stego-images are resistant to Human Visual System. Also, the embedding method presented in this paper produced stego-images that were given a low probability of detection of various statistical analysis attacks. Furthermore, the high correlation coefficient between the candidate cover images' skewness and kurtosis and the probability of detection obtained by the stego-images affirms that these properties of the candidate cover images can help in determining the most suitable candidate cover images to be used.

Cover Image Selection Technique for Secured LSB-based Image Steganography

Despite the notable performance of various existing image steganography techniques in the spatial domain, various statistical analyses such as Chi-square and RS analysis still considerably affect their over-all performance. Also, employing conventional cryptosystems to further secure the secret message adds significant overhead to the computational complexity. This paper presents a less complex and straight-forward image steganography technique that has an acceptable embedding capacity, an appropriate level of security and a significantly low probability of detection of either visual assessment or statistical analysis techniques. In this paper, the embedding process is not directly executed to the original color space of the cover image which is the RGB but rather in YCbCr color space. The Huffman encoding helped to attain the high payload capacity and the concept of quadratic residues strengthened the additional layer of security. The PSNR and SSIM results showed that after a 100 percent embedding to the LSBs of the cover image, distortions were within the satisfactory range. Also, the Chi-square and RS analysis simulation results proved that the stego-images generated by the proposed technique have a low probability of detection.

Secured Data Hiding and Sharing using Improved LSB-based Image Steganography Technique

In the Philippines, Tilapia fish farming sector is vital to the economy in providing substantial employment, income and meeting local demand for protein sources of the Filipinos. However, the possible benefits that can be derived from this industry are at stake because of the sudden occurrences of fish kill events. This can be attributed to a wide variety of natural and unnatural causes such as old age, starvation, body injury, stress, suffocation, water pollution, diseases, parasites, predation, toxic algae, severe weather, and other reasons. With the identified severe effects of fish kill events to the fish farmers, consumers and the fisheries industry, advanced measures and methods must be established to alleviate the adverse effects of this phenomenon. To solve the underlying problem on water quality monitoring system to improve freshwater aquaculture, various studies were already conducted. However, these studies merely focused on the reading and gathering of water parameters. In this paper, fuzzy logic was used to come up with a model that can analyze and generate result regarding the overall quality of the water being used in Tilapia aquaculture. The water parameters considered in this paper were temperature, dissolved oxygen, and pH level. The results of the water parameter readings using the conventional method were compared to the data that were gathered by AquaStat to test its accuracy and showed no significant difference. Also, the overall water quality obtained using the conventional method was compared to the overall water quality generated by AquaStat and obtained an accurate result. Keywords—Fuzzy logic; fuzzy sets; fish kill; freshwater aquaculture; Tilapia

http://thesai.org/Downloads/Volume13No2/Paper_65-AquaStat_An_Arduino_based_Water_Quality_Monitoring_Device.pdf

Mark Rennel D. Molato

Papers

Cover Image Selection Technique for Secured LSB-based Image Steganography

Secured Data Hiding and Sharing using Improved LSB-based Image Steganography Technique

AquaStat: An Arduino-based Water Quality Monitoring Device for Fish Kill Prevention in Tilapia Aquaculture using Fuzzy Logic