Image Steganography Approach Based Ant Colony Optimization with Triangular Chaotic Map

Bhandari, Mohan; Panday, Subash; Bhatta, Chandra Prakash; Panday, Sanjeeb Prasad

doi:10.1109/iciptm54933.2022.9753917

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…DL models have made significant advances in a variety of fields including, but not limited to, deep fakes [ 22 , 23 ], satellite image analysis [ 24 ], image classification [ 25 , 26 ], the optimization of artificial neural networks [ 27 , 28 ], the processing of natural language [ 29 , 30 ], fin-tech [ 31 ], intrusion detection [ 32 ], steganography [ 33 ], and biomedical image analysis [ 14 , 34 ]. CNNs have recently surfaced as one of the most commonly used techniques for plant disease identification [ 35 , 36 ].…”

Section: Related Workmentioning

confidence: 99%

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

et al. 2023

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Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.

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Section: Related Workmentioning

confidence: 99%

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

et al. 2023

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“…Chaotic maps find applications in various domains of information security, including stream ciphers [21,22], block ciphers [23,24], hashing [25,26], steganography, and digital watermarking [27][28][29][30]. Consequently, a wide range of chaotic maps is available for generating pseudo-random numbers, with the logistic map being a popular choice among researchers due to its extensive exploration.…”

Section: Related Workmentioning

confidence: 99%

Cryptographic Grade Chaotic Random Number Generator Based on Tent-Map

Al-Daraiseh,

Sanjalawe,

Al-E’mari

et al. 2023

JSAN

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In recent years, there has been an increasing interest in employing chaotic-based random number generators for cryptographic purposes. However, many of these generators produce sequences that lack the necessary strength for cryptographic systems, such as Tent-Map. However, these generators still suffer from common issues when generating random numbers, including issues related to speed, randomness, lack of statistical properties, and lack of uniformity. Therefore, this paper introduces an efficient pseudo-random number generator, called State-Based Tent-Map (SBTM), based on a modified Tent-Map, which addresses this and other limitations by providing highly robust sequences suitable for cryptographic applications. The proposed generator is specifically designed to generate sequences with exceptional statistical properties and a high degree of security. It utilizes a modified 1D chaotic Tent-Map with enhanced attributes to produce the chaotic sequences. Rigorous randomness testing using the Dieharder test suite confirmed the promising results of the generated keystream bits. The comprehensive evaluation demonstrated that approximately 97.4% of the tests passed successfully, providing further evidence of the SBTM’s capability to produce sequences with sufficient randomness and statistical properties.

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“…Despite its limitations, such as non-standardized image acquisition [12]; the improper labeling of data collected; and regulatory barriers [13], deep-learning (DL) research has show a significant positive impact in radiology applications, such as reducing the workload of radiologists [14]; increasing medical access in developing countries and rural areas [15]; and improving diagnostic accuracy [16]. DL models have been applied for a wide range of concerns, such as COVID-19 [17], brain tumors [18], radiation therapy [19], blood clotting [20], heart diseases [21], liver cancer [22], fin-tech [23], intrusion detection [24], vibration signals [25], organic pollutant classification [26], agriculture [27], and steganography [28]. Recent research has shown that DL models can be used successfully for diagnosing kidney abnormalities [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

et al. 2023

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Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which creates cross-sectional slices of the kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification and segmentation purposes. However, it has been difficult for clinicians to interpret the model’s specific decisions and, thus, creating a “black box” system. Additionally, it has been difficult to integrate complex deep-learning models for internet-of-medical-things devices due to demanding training parameters and memory-resource cost. To overcome these issues, this study proposed (1) a lightweight customized convolutional neural network to detect kidney cysts, stones, and tumors and (2) understandable AI Shapely values based on the Shapley additive explanation and predictive results based on the local interpretable model-agnostic explanations to illustrate the deep-learning model. The proposed CNN model performed better than other state-of-the-art methods and obtained an accuracy of 99.52 ± 0.84% for K = 10-fold of stratified sampling. With improved results and better interpretive power, the proposed work provides clinicians with conclusive and understandable results.

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Image Steganography Approach Based Ant Colony Optimization with Triangular Chaotic Map

Cited by 8 publications

References 6 publications

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

Cryptographic Grade Chaotic Random Number Generator Based on Tent-Map

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

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