Scopus Publications
- Implementation and Testing of an Image Encryption Algorithm Using a Novel Chaotic Map
S. Hanis, K. J. Jegadish Kumar
IETE Journal of Research, 2026
A secure communication scenario is one in which a third party is unable to access information shared between two parties. Cryptography is an essential part of secure communication. It is a method of storing and transmitting data in a format that only the intended recipients can read and process. The primary goals of this research are to propose a robust and practical image encryption algorithm using a novel chaotic map, implement it on a Raspberry Pi and Xilinx FPGA, and test its security using both software-based and hardware-based Side-Channel Attacks (SCA). This work unveils a cutting-edge image encryption algorithm that fuses chaotic dynamics with cellular automata to achieve high security and unpredictability. The designed algorithm was rigorously evaluated using analysis based on both hardware and software. Software-based analysis of the algorithm confirmed superior results in entropy, NPCR, UACI, and resistance to plaintext attacks, with histogram analysis further supporting its effectiveness. Hardware implementations enabled side-channel testing using EM traces and the Hamming Distance model. It is widely assumed that the amount of data that leaks over the power side-channel is proportional to the number of bits cycling between states at any given time. This attack employs statistical methods to detect changes in power traces, exposing data leakage that could lead to the derivation of the correct secret key. The results indicate a high resistance to side-channel and third-party attacks, positioning the proposed scheme as a secure and practical solution for real-world image encryption applications. - Satellite and Aerial Image Restoration Using Deep Reinforcement Learning
S. Hanis, S. Abinav Narayanan, P. Abishek Viswanath, V. Bhooshan
Fluctuation and Noise Letters, 2023
In this paper, we present a deep reinforcement learning-based method for effectively denoising satellite and aerial imagery data. Noise of various kinds and with varying noise levels contaminates satellite imagery data. The image’s quality and readability suffer when there is noise present. Therefore, it is crucial to create a network that can effectively and efficiently remove noise from the image while also preserving its quality and signal components. This paper evaluates the denoising capabilities of the deep reinforcement learning system. The proposed network is trained using the training set from the “dataset of object detection in aerial images (DOTA) dataset,” and its hyperparameters were adjusted for optimum performance. The training set from the aforementioned dataset was used to train the proposed network. The trained network was given the test set of unseen images for denoising. Statistical denoising, a common denoising technique, was used on the test dataset, and the outcomes were assessed. The same unseen images were also given to existing CNN-based denoising algorithms like denoising using CNN (DnCNN), U-shaped DnCNN (UDnCNN), and dilated U-shaped DnCNN (DUDnCNN), designed specifically for image denoising. Runtime and structural similarity index (SSIM) as well as peak signal-to-noise ratio have both been used as evaluation metrics to compare the effectiveness of various approaches. It is discovered that, when comparing the performance of various systems, the suggested system outperforms both statistical- and CNN-based denoising in terms of the evaluation metrics, PSNR and SSIM. - A New Sine-Ikeda Modulated Chaotic Key for Cybersecurity
S. Hanis
Intelligent Automation and Soft Computing, 2023
In the recent past, the storage of images and data in the cloud has shown rapid growth due to the tremendous usage of multimedia applications. In this paper, a modulated version of the Ikeda map and key generation algorithm are proposed, which can be used as a chaotic key for securely storing images in the cloud. The distinctive feature of the proposed map is that it is hyperchaotic, highly sensitive to initial conditions, and depicts chaos over a wide range of control parameter variations. These properties prevent the attacker from detecting and extracting the keys easily. The key generation algorithm generates a set of sequences using a designed chaos map and uses the harmonic mean of the generated sequences as the seed key. Furthermore, the control parameters are modified after each iteration. This change in the control parameters after each iteration makes it difficult for an attacker to predict the key. The designed map was tested mathematically and through simulations. The performance evaluation of the map shows that it outperforms other chaotic maps in terms of its parameter space, Lyapunov exponent, bifurcation entropy. Comparing the designed chaotic map with existing chaotic maps in terms of average cycle length, maximum Lyapunov exponent, approximate entropy, and a number of iterations, it is found to be very effective. The existence of chaos is also proved mathematically using Schwartz’s derivative theorem. The proposed key generation algorithm was tested using the National Institute of Standards and Technology (NIST) randomness test with excellent results. - Extended logistic map for encryption of digital images
Hanis Stanley, Amutha Ramachandran
International Journal of Nonlinear Sciences and Numerical Simulation, 2022
A novel extended logistic map has been proposed and tested mathematically for security-based applications. Because the designed extended logistic map behaves chaotically across a wide range of logistic control parameters, it is extremely difficult to predict using even the most exhaustive search methods. The map overcomes a significant drawback of simple logistic mapping, which is commonly used in encryption algorithms. The chaotic map designed was also used as a key to shuffle the pixel position of the image for the image shuffling algorithm developed. The algorithm developed produced excellent results and is adequate for providing an encrypted image in resource-constrained systems. Performance results show that this map is highly chaotic and provides high security when applied in image encryption systems. - Authenticated Encryption to Prevent Cyber-Attacks in Images
S. Hanis, N. Edna Elizabeth, R. Kishore, Ala Khalifeh
Lecture Notes on Data Engineering and Communications Technologies, 2022 - Q-Learning Based Routing in Optical Networks
Nolen B. Bryant, Kwok K. Chung, Jie Feng, Sommer Harris, Kristine N. Umeh, Michal Aibin
Canadian Conference on Electrical and Computer Engineering, 2022
The rapid increase in bandwidth demand has driven the development of flexible, efficient, and scalable optical networks. One of the technologies that allows for much more flexible resource utilization is Elastic Optical Network. However, there is a need to solve the Routing, Modulation and Spectrum Assignment (RMSA) problem. In this paper, we use reinforcement learning to improve the efficiency of the routing algorithm. More specifically, we implement an off-policy Q-learning and compare it with the state-of-the-art algorithms. The results confirm that Q-learning is highly effective when optimal results need to be found in a large search space. - Binarization of Stone Inscription Images by Modified Bi-level Entropy Thresholding
Sukanthi, S. Sakthivel Murugan, S. Hanis
Fluctuation and Noise Letters, 2021
India is rich in its heritage and culture. It has many historical monuments and temples where the walls are made of inscribed stones and rocks. The stone inscriptions play a vital role in portraying about the ancient incidents. Hence, the digitization of these stone inscriptions is necessary and contributes much for the epigraphers. Recently, the digitizing of these inscriptions began with the binarization process of stone inscriptions. This process mainly depends on the thresholding technique. In this paper, the binarization of terrestrial and underwater stone inscription images is preceded by a contrast enhancement and succeeded by edge-based filtering that minimizes noise and fine points the edges. A new method called modified bi-level thresholding (MBET) algorithm is proposed and compared with various existing thresholding algorithms namely Otsu method, Niblack method, Sauvola method, Bernsen method and Fuzzy C means method. The obtained results are evaluated with the performance metrics such as peak signal-to-noise ratio (PSNR) and standard deviation (SD). It is observed that the proposed method has an improvement of 49% and 39%, respectively, on an average by the metrics considered. - A fast double-keyed authenticated image encryption scheme using an improved chaotic map and a butterfly-like structure
Stanley Hanis, Ramachandran Amutha
Nonlinear Dynamics, 2019 - Double image compression and encryption scheme using logistic mapped convolution and cellular automata
S. Hanis, R. Amutha
Multimedia Tools and Applications, 2018 - Multiresolution based detection of macular degeneration
International Journal of Applied Engineering Research, 2016 - Detection and clutter suppression using fusion of conventional CFAR and two parameter CFAR
L. Donisha Greet, S. Harris
Icect 2011 2011 3rd International Conference on Electronics Computer Technology, 2011
