A Comprehensive Review of Traditional and Machine Learning Based Approaches in Digital Image Watermarking
Dr Aditya Kumar Sahu, Dr Aditya Kumar Sahu, Pradhan, A., Sahu, M., & Swain, G.
Source Title: The International Conference on Recent Innovations in Computing,,
View abstract ⏷
The popularity of networks and the ongoing advancement of multimedia technology have
led to an increase in the transmission of digital images through insecure channels, the need
to conserve network bandwidth, and the steady increase in public awareness of copyright
protection for multimedia information. Digital watermarking gives a powerful method for
identifying model ownership and provides a defense against such dangers, which implies
the creation of numerous eminent watermarking strategies by possible researchers.
Secure and imperceptible frequency-based watermarking for medical images
Dr Aditya Kumar Sahu, Naima, S., Boukhamla, A. Z. E., Narima, Z., Amine, K., Redouane, K. M., & Dr Aditya Kumar Sahu
Source Title: Circuits, Systems, and Signal Processing, Quartile: Q1
View abstract ⏷
Medical image security is a critical concern in the healthcare domain, and various watermarking techniques have been explored to embed imperceptible and secure data within medical images. This paper introduces an innovative frequency-based watermarking technique for medical images, utilizing the Fractional Discrete Cosine Transform (FDCT) and Schur decomposition to ensure robust and secure watermark embedding. The watermark bits are integrated by modulating the obtained Schur coefficients, thereby ensuring robust and secure watermarking without significantly altering the visual quality of the medical images. The experiments conducted on the ocular database demonstrate the capacity, imperceptibility, and robustness of the proposed method. This approach achieved a favorable trade-off between imperceptibility and information embedding capacity for ensuring the authenticity and integrity of medical images during transmission.
DWT-DCT Image Watermarking with Quantum-inspired Optimization.
Dr Aditya Kumar Sahu, Rijati, N., Ghosal, S. K., Dr Aditya Kumar Sahu, Sambas, A., & Ignatius Moses Setiadi, D. R.
Source Title: International Journal of Intelligent Engineering & Systems, Quartile: Q2
View abstract ⏷
DWT-DCT Image Watermarking with Quantum-inspired Optimization
Securing the Digital World: A Comprehensive Guide to Multimedia Security.
Dr Aditya Kumar Sahu, Deb, S., & Dr Aditya Kumar Sahu (Eds.).
Source Title: CRC Press.,
View abstract ⏷
Securing the Digital World: A Comprehensive Guide to Multimedia Security is indispensable
reading in today's digital age. With the outbreak of digital range and ever-evolving cyber
threats, the demand to protect multimedia data has never been more imperative. This book
provides comprehensive research on multimedia information security and bridges the gap
between theoretical bases and practical applications.
Secured textual medical information using a modified LSB image steganography technique.
Dr Aditya Kumar Sahu, Ogundokun, R. O., Abikoye, O. C., Ogundepo, E. A., Babatunde, A. N., Abdulahi, A. R. T., & Dr Aditya Kumar Sahu
Source Title: CRC Press.,
View abstract ⏷
Health professionals are increasingly concerned with the welfare of their patients and the security of their medical records. With the shift toward electronic methods for obtaining and recording patient information, these records have become more vulnerable to cyberattacks. Ensuring that unauthorized individuals do not gain access to sensitive medical information is paramount. This chapter aims to enhance the security of textual medical records using an improved least significant bit (LSB) steganography technique. The objective is to develop a robust medical information system that secures patient data against potential cyber threats by implementing a modified LSB procedure called circular shift LSB steganography. The proposed system was developed and programmed in the MATLAB 2018a environment. The enhancement involved rational bit shift operations to improve the traditional LSB steganography method. The performance of the modified LSB technique was assessed using key metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), and the number of shifts. The modified LSB method demonstrated superior performance compared to traditional LSB methods. Quantitative analysis revealed PSNR values ranging from 74.3458 to 80.364, indicating higher image quality and reduced distortion. MSE values ranged from 0.002391 to 0.000598, showing minimal error and high fidelity of the stego images. Additionally, the number of shifts used in the embedding process ranged from 32,640 to 88,410, enhancing the security and robustness of the stego images. The enhanced LSB steganography technique, employing rational bit shift operations, outperformed traditional LSB methods regarding robustness, capacity, and imperceptibility. The introduction of the number of shifts as a new output measure further validated the improved security and effectiveness of the proposed method. The results confirm that the updated LSB technique provides a more secure solution for protecting textual medical records against unauthorized access and cyber threats. Future research should explore further optimization of the bit shift operations to enhance the security and efficiency of the LSB steganography technique. Additionally, expanding the application of this method to other types of sensitive data and evaluating its performance in different environments and scenarios will help establish its broader utility and effectiveness.
Implementation and analysis of digital watermarking techniques for multimedia authentication
Dr Aditya Kumar Sahu, Das, S., Biswas, P., Kar, N., & Dr Aditya Kumar Sahu
Source Title: CRC Press.,
View abstract ⏷
The world we live in has evolved to a state where information and knowledge have become power. Media consumption has increased drastically with the introduction of high-speed and affordable internet services. However, the internet is not the paradise that we dream of, it is a double-edged sword that contains information as well as misinformation. Hence, the question of authenticity arises. Determining the authenticity of multimedia content has attracted the attention of several researchers. Out of these authentication algorithms, one popular area is digital watermarking, which uses watermarks to attain robustness against malicious, manipulative attacks that might destroy the authenticity of multimedia content. This chapter presents a comprehensive analysis of such recent techniques involving watermarking that help in establishing the authenticity of images, audio, and videos. A wide variety of methods for watermarking have been chosen, namely, discrete wavelet transform, singular value decomposition, quantum index modulation, LSB substitution, and some others. The performance of the techniques has been evaluated using several metrics when subjected to common attacks.
A novel image compression method using wavelet coefficients and Huffman coding
Dr Aditya Kumar Sahu, Thomas, S., Krishna, A., Govind, S., & Sahu, A. K
Source Title: Journal of Engineering Research, Quartile: Q2
Opposing agents evolve the research: a decade of digital forensics. Multimedia Tools and Applications
Dr Aditya Kumar Sahu, Raman, R., Dr Aditya Kumar Sahu, Nair, V. K., & Nedungadi, P.
Source Title: Multimedia Tools and Applications, Quartile: Q1
FDCT-Based Watermarking for Robust and Imperceptible Medical Image Protection
Source Title: Intelligence-Based Medicine, Quartile: Q2
View abstract ⏷
Security in medical imaging is a pivotal concern within the healthcare domain, prompting exploration into various watermarking techniques designed to embed imperceptible and secure data within medical images. In this study, we introduce a frequency-based medical image watermarking approach that leverages the Fractional Discrete Cosine Transform (FDCT), Mellin Transform, and Schur decomposition to extract the frequency content of the image. This process is followed by the selection of low-frequency coefficients for further transformation using Schur decomposition. The integration of watermark bits occurs through modulation of the obtained Schur coefficients, ensuring robust and secure watermarking without significantly altering the visual quality of the medical images. The experiments conducted on the ocular database illustrate the capacity, imperceptibility, and robustness of the proposed method. The proposed approach achieves a PSNR of 39.38 dB and SSIM of 0.9998, demonstrating excellent imperceptibility with a capacity of 0.07031 bits per pixel (BPP). The method is robust against various attacks, including JPEG compression, noise addition, and geometric transformations, with NCC values consistently above 0.85 for most common image processing operations. This approach successfully achieves a favorable trade-off between imperceptibility and information embedding capacity, ensuring the authenticity and integrity of medical images during transmission.
Robust and imperceptible medical image watermarking for telemedicine applications based on transform-domain and neural clustering techniques
Source Title: Journal of the Franklin Institute, Quartile: Q1
Robust medical image watermarking based on Ridgelet transform and Ant Colony Optimization for telemedicine security
Source Title: Systems and Soft Computing, Quartile: Q2
A Qualitative Analysis of the Internet of Everything (IoE) and Industrial IoT (IIoT) in the Context of Industry 5.0
Dr Aditya Kumar Sahu, Dr Aditya Kumar Sahu, Anitha, K., Hemalatha, J., & Sahu, M.
Source Title: The International Conference on Recent Innovations in Computing,,
View abstract ⏷
The Internet of Everything (IoE) and the Industrial Internet of Things (IIoT) represent transformative paradigms in the world of connected technology, extending the reach of the Internet to encompass physical devices, data, people, and processes. IoE and IIoT play crucial roles in Industry 5.0, emphasizing the symbiotic relationship between humans and automation. These paradigms offer substantial benefits but also pose challenges that require attention. Recent developments in edge computing, AI, ML, and 5G networks continue to enhance the capabilities of IoE and IIoT, ushering in a more connected, intelligent, and efficient future. This qualitative research study aims to investigate the intricate dynamics of the Internet of Everything (IoE) and Industrial Internet of Things (IIoT) to explore their classification, benefits within the framework of Industry 5.0, challenges faced, diverse applications, and recent developments. Drawing on the insights gathered from meticulous content analysis of pertinent literature, this research offers valuable perspectives on these transformative technologies and their impact on various sectors.
A systematic survey on TPE schemes for the cloud: Classification, Challenges and Future Scopes. IEEE Access.
Dr Aditya Kumar Sahu, Chowdhury, K., Deb, S., Roy, K. S., Podder, D., & Dr Aditya Kumar Sahu
Source Title: IEEE Access, Quartile: Q1
Enhancing security and ownership protection of neural networks using watermarking techniques: A systematic literature review using prisma
Dr Aditya Kumar Sahu, Ogundokun, R. O., Abikoye, C. O., Dr Aditya Kumar Sahu Akinrotimi, A. O., Babatunde, A. N., Sadiku, P. O., & Olabode, O. J.
Source Title: Multimedia Watermarking,
View abstract ⏷
The rise of artificial intelligence (AI) and machine learning (ML) has prompted concerns regarding the intellectual property (IP) protection of neural networks (NNs). A proposed solution is watermarking, which incorporates a unique identifier into a NN. However, the effectiveness of watermarking methods in enhancing privacy and ownership secrecy remains questionable. This study intended to evaluate the efficacy of watermarking techniques for enhancing the security and ownership protection (SOP) of NNs. An exhaustive search of scholarly databases for peer-reviewed journal articles and conference proceedings was conducted in accordance with PRISMA standards. Eligible papers evaluated the efficacy of watermarking techniques used to protect NNs. Twenty research articles using various watermarking techniques, including digital watermarking (DW), reversible watermarking (REW), and robust watermarking (ROW), were analyzed. Various performance indicators, such as detection rate (DR), robustness, and distortion, were employed to evaluate the applicability of each method. The results demonstrated that watermarking techniques effectively protected the intellectual property of NNs with minimal impact on performance. However, the need for specialized apparatus and the difficulty of incorporating watermarks into deep neural networks (DNN) hampered their implementation. To improve the practicability and effectiveness of watermarking techniques, additional research is required. Researchers, professionals, and policymakers should consider watermarking to safeguard the intellectual property of NNs in a variety of domains, including finance, healthcare, and national security.
A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement
Dr Aditya Kumar Sahu, Hameed, M. A., Hassaballah, M., Abdelazim, R., & Sahu, A. K
Source Title: International Journal of Cognitive Computing in Engineering, Quartile: Q1
Fake news research trends, linkages to generative artificial intelligence and sustainable development goals
Dr Aditya Kumar Sahu, Raman, R., Nair, V. K., Nedungadi, P., Dr Aditya Kumar Sahu, Kowalski, R., Ramanathan, S., & Achuthan, K.
Source Title: Helion, Quartile: Q1
A Watermark Challenge: Synthetic Speech Detection
Dr Aditya Kumar Sahu, Narla, V. L., Suresh, G., Dr Aditya Kumar Sahu, & Kollati, M.
Source Title: Multimedia Watermarking,
View abstract ⏷
Synthetic audio signal generation is an easier task with the help of open-source software and deep learning tools. These freely available tools are maliciously used and it negatively impacts society. To overcome this problem, we made an attempt to develop a synthetic audio signal detector. This work measures statistical and entropy features on discrete wavelength transform (DWT) transformed input audio signal. These features are trained and tested using supervised classification techniques. The proposed work is validated on a publicly available synthetic audio database. The accuracy of the proposed work is 99.0% and is compared with the state-of-the-art works validating superiority over other existing methods.
Multimedia Watermarking: Latest Developments and Trends
View abstract ⏷
Multimedia watermarking is a key ingredient for integrity verification, transaction tracking, copyright protection, authentication, copy control, and forgery detection. This book provides an extensive survey from the fundamentals to cutting-edge digital watermarking techniques. One of the crucial aspects of multimedia security is the ability to detect forged/tampered regions from the multimedia object. In this book, we emphasized how tampering detection, localization, and recovery of manipulated information not only limits but also eliminates the scope of unauthorized usage. Finally, this book provides the groundwork for understanding the role of intelligent machines and blockchain in achieving better security in multimedia watermarking.
Multimodal imputation-based stacked ensemble for prediction and classification of air quality index in Indian cities
Dr Aditya Kumar Sahu, Rao, R. S., Kalabarige, L. R., Alankar, B., & Sahu, A. K
Source Title: Computers and Electrical Engineering, Quartile: Q1
View abstract ⏷
Nowadays, monitoring and predicting the air quality is very much needed to identify and control the adverse health effects due to the low air quality, especially in developing countries like India. Recently, it has been an interesting research topic to predict the air quality index (AQI) values and levels using machine learning algorithms. In this paper, we proposed a multimodal imputation based stacked ensemble (MISE) model to classify and predict the quality of air. The multimodel imputation is constructed using various imputation techniques such as KNN Impute, MICE and SVD Impute. We experimented the proposed model with various tree based algorithms such as Random Forest, XGBoost and Extra Tree to identify the best classification and regression model for the dataset. The Stacked ensemble is developed using above algorithms for classifying the AQI bucket. Based on the experimentational study, it is observed that stacked ensemble performed better in classifying AQI with an accuracy of 96.45% using SMOTE method. The proposed stacking model also performed better than other classifier with an accuracy of 91.13% on the imbalanced data. The proposed method MISE is also applied on the dataset for identifying the AQI score using tree based regression algorithms. The stacked ensemble performed better with an R2 score of 0.9687, MAE of 0.1052 and MSE of 0.0272 compared to existing models.
Advancements in artificial intelligence for biometrics: a deep dive into model-based gait recognition techniques.
Dr Aditya Kumar Sahu, Parashar, A., Parashar, A., Shabaz, M., Gupta, D., Dr Aditya Kumar Sahu, & Khan, M. A
Source Title: Engineering Applications of Artificial Intelligence, Quartile: Q1
Robust medical and color image cryptosystem using array index and chaotic S-box
Dr Aditya Kumar Sahu, Podder, D., Deb, S., Banik, D., Kar, N., & Dr Aditya Kumar Sahu
Source Title: Cluster Computing, Quartile: Q1
View abstract ⏷
Providing robust security within an image cryptosystem during network communication is more essential than ever, highlighting the fundamental aspects of confusion and diffusion. This study presents a novel approach for securely transmitting images through insecure channels, using array index manipulation for scrambling and simple pixel-level confusion and diffusion through XOR operations, effectively involving row and column permutation to achieve robust scrambling. This method operates two layers of confusion by rearranging array indexes using a Tent map and constructing an S-box algorithm derived from the Henon map, while the diffusion process uses a pseudo-random sequence generator based on the Henon map, with their chaotic dynamics analyzed through investigations of Lyapunov exponents and bifurcation diagrams. The expanded chaotic range and improved effects of chaotic maps produce new S-boxes with satisfactory cryptographic performance, including balancedness and non-linearity, with S-box3 achieving the highest non-linearity at 108. Following a performance and security assessment of the proposed technique, it has been found that the entropy value for the cipher image is nearly 7.998; also, the NPCR and UACI values for the cipher images are 99.6 and 33.4, respectively, and it demonstrates closeness to the ideal value. Finally, the proposed method demonstrates high randomness, undergoes evaluation using the NIST test suite, has good operation efficiency, and exhibits resilience against various attack forms such as statistical, differential, data-loss, and noise attacks, affirming its security and relevance for real-time cryptosystems.
Robust data hiding method based on frequency coefficient variance in repetitive compression
Dr Aditya Kumar Sahu, Solak, S., Abdirashid, A. M., Adjevi, A., & Dr Aditya Kumar Sahu
Source Title: Engineering Science and Technology, an International Journal,, Quartile: Q1
View abstract ⏷
Sharing accurate and lossless images with higher quality through digital mediums is challenging, particularly, images shared on social media platforms can serve as good carriers for sending hidden data. However, social media platforms apply severe compression when transferring images end-to-end to serve efficient network transporting bandwidth and provide enough storage space to the users. Within the scope of this research, the proposed method introduces a novel approach by combining cryptographic and steganographic techniques, providing a robust solution to protect hidden data even when subjected to repeatedly compression. The method first encrypts the secret data to be hidden using the Advanced Encryption Standard Cipher Block Chaining (AES-CBC) technique. Then, data hiding is performed on the coefficients obtained by applying Discrete Cosine Transform (DCT) to repeatedly compressed JPEG images, coefficients that are minimally affected by compression or remain unaffected are specifically selected for data hiding. Therefore, secret data is extracted with high accuracy. Experimental results show that the proposed method outperforms the state-of-the-art in achieving robust and effective data hiding techniques based on bit error rate.
Introduction to the special section on recent advances in multimedia forensics for cyber security and data tampering (VSI-forens).
Source Title: Computers and Electrical Engineering, Quartile: Q1
Multimodal Imputation based Multimodal autoencoder framework for AQI classification and prediction of Indian cities
Dr Aditya Kumar Sahu, Rao, R. S., Kalabarige, L. R., Holla, M. R., & Dr Aditya Kumar Sahu
Source Title: IEEE Access, Quartile: Q1
View abstract ⏷
Rising urbanization necessitates robust air quality monitoring and prediction systems, particularly in developing nations like India, to mitigate adverse health impacts. Previous research primarily focused on machine learning algorithms for Air Quality Index (AQI) prediction and classification. We propose a novel MI-MMA-XGB which coupled features of multimodal imputer(MI) with the features of multi-modal autoencoder (MMA) and fed to an XGBoost(XGB) algorithm for AQI prediction and classification. Moreover, imputation approaches namely, KNN, MICE, and SVD were employed to address problems with null values and outliers. Furthermore, SMOTE is employed to balance the imputed data and then the model was trained on both balanced and unbalanced imputed data to extract predictive features. In this process, our model MI-MMA-XGB achieves significant accuracy, reaching 97.14% and 93.87% with and without SMOTE, respectively. Additionally, it attains an score of 0.9578 and an RMSE of 0.203 for AQI prediction in Indian cities. The proposed model outperforms baseline models in both classification and regression tasks across various evaluation metrics.
Data privacy protection using lucas series based hybrid reversible watermarking approach
Dr Aditya Kumar Sahu, Rupa, C., Malleswari, R. P., Sultana, S. A., Abbas, M., & Dr Aditya Kumar Sahu
Source Title: IEEE Access, Quartile: Q1
Improved multiview biometric object detection for anti spoofing frauds.
Dr Aditya Kumar Sahu, Asmitha, P., Rupa, C., Nikitha, S., Hemalatha, J., & Dr Aditya Kumar Sahu
Source Title: Multimedia Tools and Applications, Quartile: Q1
View abstract ⏷
Computer vision and deep learning are essential in human authentication. It provides answers to numerous issues faced in the real world. Moreover, it has a great potential in detecting and recognizing the biometrics. It plays a significant role in reducing frauds such as spoofing, identification (ID) theft, and masking types of issues, which are difficult to perform manually, and many case studies used deep learning algorithms (DLA) like viola-jones, AlexNet, and Tiny YOLO3 but the main limitations of these studies are that they are not capable of giving high accuracy and robustness in the multi-face scenarios. So, in this article, an enhanced ArcFace (Additive Angular Margin loss) referred to as Improved ArcFace (I-AF) utilizes Convolution Neural Network (CNN) as its base architecture for feature extraction and RetinaFace are combined to overcome the above limitation, whereas RetinFace is for detecting and I-AF is for recognizing and authenticating human faces. It gives robust and accurate results while dealing with multi-faces. To evaluate the performance of the human monitoring system, it is implemented on real-time student data in a classroom to track the attendance of individuals. The faces of individuals in a classroom are detected and each detected face will be recognized and finally the result will be mapped for the attendance of individuals. To improve accuracy, the data set termed as labeled data, which contains images of students, is trained using I-AF. The system is 97% accurate, which is better than other methods.
Digital to quantum watermarking: A journey from past to present and into the future
Source Title: Computer Science Review, Quartile: Q1
View abstract ⏷
With the amplification of digitization, the surge in multimedia content, such as text, video, audio, and images, is incredible. Concomitantly, the incidence of multimedia tampering is also apparently increasing. Digital watermarking (DW) is the means of achieving privacy and authentication of the received content while preserving integrity and copyright. Literature has produced a plethora of state-of-the-art DW techniques to achieve the right balance between its performance measuring parameters, including high imperceptibility, increased watermarking ability, and tamper-free recovery. Meanwhile, during the vertex of DW, scientific advances in quantum computing led to the emergence of quantum-based watermarking. Though quantum watermarking (QW) is in its nascent stage, it has become captivating among researchers to dive deep inside it. This study not only investigates the performance of existing DW techniques but also extensively assesses the recently devised QW techniques. It further presents how the principles of quantum entanglement and superposition can be decisive in achieving superior immunity against several watermarking attacks. To the best of our knowledge, this study is the unique one to present a comprehensive review of both DW as well as QW techniques. Therefore, the facts presented in this study could be a baseline for the researchers to devise a novel DW or QW technique.
Breast cancer image classification by using HCNN and LeNet5
Dr Aditya Kumar Sahu, Patro, P., Fathima, S. H., Harikishore, R., & Sahu, A. K
Source Title: Discover Sustainability, Quartile: Q2
View abstract ⏷
Medical data from many sectors has greatly increased during the last 10 years. One of the main causes of death and illness among women worldwide is breast cancer. Breast cancer (BC) is one of the most common cancers in women; the death rate is high and holds the second position next to lung cancer. Breast cancer develops when cells in the mammary glands and the ducts that transfer milk to the nipple grow out of control. This is the first step in the progression of the disease. However, the time complexity of the available techniques is immense due to the many processes. Additionally, the current research attempts to improve the computation time involved in the detection process. Therefore, an effective hybrid deep learning model is introduced to improve the prediction performance and reduce the time consumption compared to the machine learning model. The breast cancer dataset, obtained from Kaggle, is used as the input data. A Wiener filter preprocessing technique is applied to enhance the image quality, with an active Wiener filter employed for this purpose. The segmentation step is achieved using a Modified Watershed Algorithm, which isolates the region of interest within the images. Finally, classification is performed using a hybrid deep learning model. This model combines a Convolutional Neural Network (CNN) with an Enhanced Recurrent Neural Network (ERNN), leveraging the strengths of both architectures. According to experimental results, the proposed Hybrid Convolutional Neural Network (HCNN) model achieves an accuracy of 96.12%, a precision of 96.99%, a recall of 97.52%, and an F-measure of 97.25%, outperforming other existing models.
Enhancing cloud communication security: a blockchain-powered framework with attribute-aware encryption.
Dr Aditya Kumar Sahu, Kallapu, B., Dodmane, R., Thota, S., & Dr Aditya Kumar Sahu
Source Title: Electronics, Quartile: Q1
Logistic-map based fragile image watermarking scheme for tamper detection and localization
Dr Aditya Kumar Sahu, Dr Aditya Kumar Sahu, Hassaballah, M., Rao, R. S., & Suresh, G
Source Title: Multimedia Tools and Applications, Quartile: Q1
Pixel recurrence based image watermarking for block based integrity verification.
Dr Aditya Kumar Sahu, Murapaka, R. R., Kumar, A. P., & Dr Aditya Kumar Sahu
Source Title: International Journal of Electronic Security and Digital Forensics, Quartile: Q1
Intelligent data classification using optimized fuzzy neural network and improved cuckoo search optimization
Dr Aditya Kumar Sahu, Patro, P., Kumar, K., Kumar, G. S., & Dr Aditya Kumar Sahu
Source Title: Iranian Journal of Fuzzy Systems, Quartile: Q1
View abstract ⏷
In data mining, classification is one of the most important steps in predicting the target class. Classification is performed by an improved model in existing work in which feature selection is performed based on the bat optimization method to increase the classification accuracy. And an Enhanced Neural Network is used for classification which includes Intuitive, Interpretable Correlated-Contours fuzzy rules. And an effective model is created based on the extraction of fuzzy rules, where data partitioning is performed via a similarity-based directional component. However, the dataset used for experimentation is noisy as well as incomplete data values. Due to incompleteness, knowledge discovery is obstructed and the result of classification is affected as well. And bat provides very slow convergence and easily falls into local optima. To solve this issue, an improved framework is introduced in which missing value imputation is performed by using k means clustering, and then for feature selection, an improved cuckoo search optimization is used. An enhanced classifier based on fuzzy logic and Alex Net neural network structure (F-ANNS) is used for classification and hybrid Ant Colony Particle Swarm Optimization (HASO) is used for optimizing parameters of the AlexNet neural network classifier. The results show that the proposed work is more effective in precision, recall, accuracy, and f-measure as shown by experimental results.
A Novel and Secure Fake-Modulus Based Rabin-? Cryptosystem
Dr Aditya Kumar Sahu, Ramesh, R. K., Dodmane, R., Shetty, S., Aithal, G., Sahu, M., & Dr Aditya Kumar Sahu
Source Title: Cryptography, Quartile: Q1
Towards improving the performance of blind image steganalyzer using third-order SPAM features and ensemble classifier
Dr Aditya Kumar Sahu, Hemalatha, J., Sekar, M., Kumar, C., Gutub, A., & Dr Aditya Kumar Sahu
Source Title: Journal of Information Security and Applications,
View abstract ⏷
The success rate for blind or universal steganalysis lies in the ability to extract the statistical footprints of image features. Further, the choice of machine learning (ML) algorithm is crucial to distinguish the stego image more precisely from the untouched clean images. Literature suggests that most steganalysis approaches report less favorable detection accuracy despite considering many features. This study presents a three-step process to accurately identify the clean and stego images to solve this issue. We used the curvelet denoising as an initial phase during the first step to suppress the natural noise residuals (NRs) by producing the stego NRs. Secondly, it extracts the Third-order Markov-chain sample transition probability matrices as features. Finally, the oblique decision tree ensemble using a multisurface proximal support vector machine (SVM) classifier has been utilized to achieve greater detection accuracy than the state-of-the-art classifiers. The experiments are performed on an extensive database comprising clean and stego images generated from nine embedding schemes with varying payloads. The experimental results suggest that an accuracy of 93.12 has been achieved using the proposed Third order subtractive pixel adjacency matrix (SPAM) features with an ensemble classifier.
Hazardous Asteroid Prediction using Majority Voting Technique
Dr Aditya Kumar Sahu, Reddy, C. V. R., Sai, T. N., Sushanth, V., Muvva, S., Rani, D. R., & Dr Aditya Kumar Sahu
Source Title: 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS),
Dual image-based reversible fragile watermarking scheme for tamper detection and localization
Dr Aditya Kumar Sahu, Dr Aditya Kumar Sahu, Sahu, M., Patro, P., Sahu, G., & Nayak, S. R.
Source Title: Pattern Analysis and Applications, Quartile: Q1
Secure reversible data hiding using block-wise histogram shifting
Dr Aditya Kumar Sahu, S. Kamil, M. Sahu, Raghunadan K , A K Sahu
Source Title: Electronics, Quartile: Q1
A Study on Content Tampering in Multimedia Watermarking
Dr Aditya Kumar Sahu, Dr Aditya Kumar Sahu Umachandran, K., Biradar, V. D., Comfort, O., Sri Vigna Hema, V., Odimegwu, F., & Saifullah, M. A.
Source Title: SN Computer Science, Quartile: Q1
View abstract ⏷
Technological progresses offer more occasions for tampering outbreaks. Lithography services at affordable prices, in aggregation with open software tools to influence changes in digital spaces, invigorated amateurs to oblige to fabricating and imitating. Tampering has reached a level of cleverness that leaves negligible trace with the pace of progress happening with editing technology, luring the next generation. However tampering with technology violates intellectual property rights that can be treated to cost dearly, including severe retributions. At the same time, accountability for evading, aiding the evasion of technology, and restricting access that could impede the infringement of the content are new protection activities, which were not present during the pre-digital age. Digitalization shrinks the cost of content development, nevertheless availability of pirated content is also on the rise due to ease of copy, transform and distribution. Surveillance is a big dataset, with arrangements from various sources at diverse scenarios that are critical to events, therefore, susceptible errors such as defocusing, occlusion and displacement. The forensic study is thus correlated, for prediction using multi-task joint model through convolutional neural network (CNN), as they are open to access in metadata, also its alteration through ease of EXIF tools provide innumerable opportunities to tamper contents, thus tough to identify, except after severe inquiries. In this study, we present a brief overview of recent status with respect to the content tampering using several advanced tools.
Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection
Dr Aditya Kumar Sahu, S. Srinivasan, A. Karuppanan, S. Mathivanan, P. Jayagopal, JC. Babu, Dr Aditya Kumar Sahu
Source Title: Diagnostics, Quartile: Q2
View abstract ⏷
Every year, cervical cancer is a leading cause of mortality in women all over the world. This cancer can be cured if it is detected early and patients are treated promptly. This study proposes a new strategy for the detection of cervical cancer using cervigram pictures. The associated histogram equalization (AHE) technique is used to improve the edges of the cervical image, and then the finite ridgelet transform is used to generate a multi-resolution picture. Then, from this converted multi-resolution cervical picture, features such as ridgelets, gray-level run-length matrices, moment invariant, and enhanced local ternary pattern are retrieved. A feed-forward backward propagation neural network is used to train and test these extracted features in order to classify the cervical images as normal or abnormal. To detect and segment cancer regions, morphological procedures are applied to the abnormal cervical images. The cervical cancer detection system's performance metrics include 98.11% sensitivity, 98.97% specificity, 99.19% accuracy, a PPV of 98.88%, an NPV of 91.91%, an LPR of 141.02%, an LNR of 0.0836, 98.13% precision, 97.15% FPs, and 90.89% FNs. The simulation outcomes show that the proposed method is better at detecting and segmenting cervical cancer than the traditional methods.
Chaotic-Map Based Encryption for 3D Point and 3D Mesh Fog Data in Edge Computing
Dr Aditya Kumar Sahu, K. R Raghunandan, R. Dodmane, K. Bhavya, K. Rao, Dr Aditya Kumar Sahu
Source Title: IEEE Access, Quartile: Q1
View abstract ⏷
Recent decades have seen dramatic development and adoption of digital technology. This technological advancement generates a large amount of critical data that must be safeguarded. The security of confidential data is one of the primary concerns in fog computing. As a result, achieving a reliable level of security in the fog computing environment is crucial. In this context, 3D point and mesh fog data are becoming increasingly popular among the various types of data stored in the fog. Data encryption using chaotic behavior is one of the preferred research areas due to its unique properties, such as randomness, determinism, sensitivity to initial conditions, and ergodicity. In this paper, we have taken advantage of this chaotic behavior to achieve higher security. This study presents a novel approach for protecting the privacy of 3D point and mesh fog data. Initially, the fog data coordinates are transformed using the sequence generated by the chaotic behavior. Then, bifurcation analysis is used to depict the enhanced scope of the proposed map. The quality of the proposed chaotic system is assessed using metrics such as the Lyapunov exponent and approximate entropy. Results show that the proposed encryption framework performs superior when subjected to brute-force and statistical attacks. Further, the designed framework produces better results than the prior literature.
A logistic map based blind and fragile watermarking for tamper detection and localization in images
Source Title: Journal of Ambient Intelligence and Humanized Computing, Quartile: Q1
View abstract ⏷
In real-time data transmission, the protection of multimedia content from unauthorized access is pivotal. In this context, digital watermarking techniques have drawn significant attention from the past few decades. However, most of the reported techniques fail to achieve a good balance among the perceptual transparency, embedding capacity (EC), and robustness. Besides, tamper detection and localization are the two crucial aspects of any authentication based watermarking technique. This paper proposes a logistic map based fragile watermarking technique to efficiently detect and localize the tampered regions from the watermarked image (WI). The proposed technique takes advantage of the sensitivity property of the logistic map to generate the watermark bits. Next, these watermark bits are embedded in the rightmost least significant bits (LSBs) by performing the logical XOR operation between the first intermediate significant bits (ISBs) and the watermark bits. Simulation results show that the proposed technique can produce high quality WI with an average peak signal-to-noise ratio (PSNR) of 51.14 dB. Further, the proposed technique can efficiently detect and locate the tampering regions from the image with a high true positive rate, low false positive and negative rate. Additionally, the proposed technique exhibits an excellent ability to resist various intentional and unintentional attacks which makes it suitable for real-time applications.
High Fidelity based Reversible Data Hiding using Modified LSB Matching and Pixel Difference
Source Title: Journal of King Saud University-Computer and Information Sciences, Quartile: Q1
View abstract ⏷
Owing to the inefficiency to hide large volume of secret data for the reversible data hiding (RDH) image steganography approaches, we propose two improved RDH based approaches, such as (1) improved dual image based least significant bit (LSB) matching with reversibility, and (2) n-rightmost bit replacement (n-RBR) and modified pixel value differencing (MPVD). The first approach extends the ability of LSB matching with reversibility using dual images. Whereas the second approach utilizes four identical cover images for secret data embedding using two phases, such as (1) n-rightmost bit replacement (n-RBR) and (2) modified pixel value differencing (MPVD). In the n-RBR phase, n bits of secret data are embedded in the pair of two neighboring pixels of the first two identical images, where 1???n???4. Correspondingly, the MPVD phase uses the third and fourth identical images for hiding the secret data. Experimental results with respect to peak signal-to-noise ratio (PSNR), embedding capacity (EC), structural similarity index (SSIM), and the comparative analysis with recently proposed state-of-art approaches exhibit the superiority of the proposed approach. Besides reversibility, the proposed approach ensures high fidelity to salt and pepper (S&P) noise, RS analysis, and pixel difference histogram (PDH) analysis.
Data Hiding Using PVD and Improving Security Using RSA
Dr Aditya Kumar Sahu, Jeyaprakash, H., Kartheeban, K., Dr Aditya Kumar Sahu, & Chokkalingam, B.
Source Title: Journal of Applied Security Research, Quartile: Q1
View abstract ⏷
Steganography deals with hiding information, which offers ultimate security in defense, profitable usages, thus sending the imperceptible information, will not be bare or distinguished by others. In this paper, a multidirectional PVD hiding scheme with RSA algorithm is proposed. Secret bits are embedded in three directions of a color image by dividing the non-overlapping blocks into R, G, b channels by selecting the minimum pixels of each block regrouping. In order to ensure the security of an stego-image, the work proposed by scheme is used, and in addition to that RSA algorithm is proposed.
Keywords:
Machine learning-based method for recognition of paddy leaf diseases
Dr Aditya Kumar Sahu, G Suresh, NV Lalitha, Dr Aditya Kumar Sahu
View abstract ⏷
Agriculture sector has been facing extensive challenges such as climate instability, cropping pattern, inadequate use of fertilizers, disease identification, and promoting new technologies. Owing to this, recognizing plant disease is one of the leading concerns in boosting productivity. In this paper, an efficient image processing technique has been utilized to classify diseases that occur in rice plant. Initially, background portion of an RGB rice plant image is removed in preprocessing phase. Next, three different clusters are obtained from the image through K-means clustering algorithm. Later, the diseased portions from these clusters of image are retrieved using histogram and color values. Color and texture features are calculated on diseased images. Finally, these obtained features are subjected to the classification phase using a support vector machine (SVM) classifier. Experimentation validates that the proposed detection method through SVM achieves maximal accuracy of 83.3% by outperforming other existing methods.
Local binary pattern based reversible data hiding
Dr Aditya Kumar Sahu, M. Sahu, N. M. Padhy, S.S. Gantayat, Dr Aditya Kumar Sahu
Source Title: CAAI Transactions on Intelligence Technology, Quartile: Q1
False-Positive-Free SVD Based Audio Watermarking with Integer Wavelet Transform
Dr Aditya Kumar Sahu, N. V. Lalitha, G. Suresh, D. P. Gangwar; Dr Aditya Kumar Sahu
Source Title: Circuits, Systems & Signal Processing, Quartile: Q1
View abstract ⏷
Singular Value Decomposition (SVD) became a promising approach for developing digital media watermarking techniques due to stability and higher energy packing nature of singular values. Nevertheless, SVD based watermarking techniques suffers from false positive problem (FPP) when singular vectors are shared for extraction. Eliminating FPP in the development of digital audio watermarking (DAW) is still a challenging task. In this work, SVD based schemes and their vulnerability to FPP are studied, analyzed, and elucidated in detail. Further, a false positive free SVD based DAW scheme has been devised in Integer Wavelet Transform (IWT) domain. Audio is partitioned into segments. Each audio segment is transformed using IWT and SVD is applied on Arnold transformed watermark. Principal Component (PC) is obtained with the product of singular vector matrix and singular values matrix. Transformed audio is modified based on PC of watermark image. The developed scheme has been tested on benchmark dataset and it maintains imperceptibility, robustness, and capacity as per standards. The developed scheme has achieved resilience against signal processing attacks. Consequently, this DAW scheme helps in forensic examination of audio recording for authentication purpose.
Performance analysis of image steganography techniques
Dr Aditya Kumar Sahu, M. Sahu, N. Padhy, S. S. Gantayat, Dr Aditya Kumar Sahu
Source Title: Second International Conference on Computer Science, Engineering and Applications, IEEE conference,
View abstract ⏷
Information hiding is the most interesting and prominent field of data security. With the evolution of computational infrastructure, covert communication techniques have received an inclusive acknowledgment among researchers as well as common participants of data communication. Among all, an image steganography technique which is a field of data hiding is one of the foremost choices among experts. The foremost defiance in scheming a steganographic system is to preserve an adequate equilibrium among the measures. The objective of this study is to present a all-inclusive survey of disparate existing images steganography techniques (ISTs) with regard to different performance assessment standards, such as (1) camouflage image (CI) quality, (2) capacity, and (3) robustness to different attacks. Further, the underlying challenges and future directions are also highlighted.
Data hiding based on frequency domain image steganography
Dr Aditya Kumar Sahu, Abdirashid, A. M., Solak, S., & Dr Aditya Kumar Sahu
Source Title: European Journal of Science and Technology,
View abstract ⏷
The rapid development in the field of communication and technology has led to a heavy increase in the data produced in digital environment, and the need to take various active security measures has arisen in the case of robust secured data and end-to-end transmission. In line with this need, widely used methods developed are Cryptography which scrambles the data to secure the information, and Steganography techniques aiming to conceal data in digital objects so third parties cannot detect the transmitted content. Image steganography techniques can be divided into two groups: spatial domain and transform domain. Spatial domain techniques embed messages directly in the intensity of the pixels, while the transform domains also known as frequency domain images are first transformed and then the message is embedded in the image. Many practices have been offered and developed in the literature to provide secure transmission of the data. In this paper, data-hiding techniques based on frequency domain image steganography has proposed. Among these techniques, the working principle of DFT has been explained, DCT and DWT techniques performed to embed and extract secret data. As a result of the proposed methods, it has been achieved to obtain the maximum data embedding capacity inthe cover image by minimizing the distortion in the stego image. It has been observed that the size and quality of the JPEG stego images have been obtained without deterioration after the data is hidden. The experimental results show successful extraction of the accurate secret message. Some good PSNR of ?50 dB and SSIM results of the proposed methods can represent successful restoration for the same image.
Latest Trends in Deep Learning Techniques for Image Steganography
Dr Aditya Kumar Sahu, V Kumar, S Sharma, C Kumar, Dr Aditya Kumar Sahu
Source Title: International Journal of Digital Crime and Forensics, Quartile: Q3
View abstract ⏷
The development of deep convolutional neural networks has been largely responsible for the significant strides forward made in steganography over the past decade. In the field of image steganography, generative adversarial networks (GAN) are becoming increasingly popular. This study describes current development in image steganographic systems based on deep learning. The authors' goal is to lay out the various works that have been done in image steganography using deep learning techniques and provide some notes on the various methods. This study proposed a result that could open up some new avenues for future research in deep learning based on image steganographic methods. These new avenues could be explored in the future. Moreover, the pros and cons of current methods are laid out with several promising directions to define problems that researchers can work on in future research avenues.
Improving grayscale steganography to protect personal information disclosure within hotel services
Source Title: Multimedia Tools and Applications, Quartile: Q1
Shadow image based reversible data hiding using addition and subtraction logic on the LSB planes
Dr Aditya Kumar Sahu, M. Sahu, N. Padhy, S. S. Gantayat, Dr Aditya Kumar Sahu
Source Title: Sensing and Imaging, Quartile: Q2
Multi-directional block based PVD and modulus function image steganography to avoid FOBP and IEP
Dr Aditya Kumar Sahu, Dr Aditya Kumar Sahu, G. Swain, Monalisa Sahu, J Hemalatha
Source Title: Journal of Information Security and Applications, Quartile: Q1
Digital image steganography and steganalysis: A journey of the past three decades.
Source Title: Open Computer Science, Quartile: Q2
An improved method for high hiding capacity based on LSB and PVD
Source Title: Digital Media Steganography,
Dual Stego-imaging based Reversible Data Hiding using Improved LSB Matching
Source Title: International Journal of Intelligent Engineering and Systems, Quartile: Q2
View abstract ⏷
Since the inception of the reversible data hiding (RDH) concept, it has been a compelling topic in the field of data hiding. Being reversible, it has the ability to restore the original image followed by the successful retrieval of the secret data, at the receiving side. The concept of the dual stego-image based RDH technique utilizes two identical images of the original image for concealing the secret data, has gained wide compliance. Therefore, someone with both the stego-images can only extract the concealed data. In this paper, two improved dual imaging based RDH techniques, such as (1) dual stego-image based pixel pair LSB matching with reversibility, and (2) dual stego-image based modified LSB matching with reversibility, are proposed. In technique 1, at first two mirrored images are obtained from the original image. Then, using the pair of two consecutive pixels from the original image, the mirrored images pixels are modified using LSB matching technique. Later, these pixel pairs are readjusted to ensure reversibility at the receiving side. Similarly, technique 2 utilizes each original pixel to generate two distinct stego- pixels using modified LSB matching. The experimental result shows that the technique 1 maintains excellent peak signal-to-noise ratio (PSNR) of 51.29 dB and 51.30 dB for the two stego-images with hiding capacity (HC) of 524288 bits. At the same time, technique 2 offers 51.19 dB and 49.44 dB of PSNR while exhibiting the equal HC. Further, investigation with various image quality assessment (IQA) metrics like quality index (QI), and structural similarity index (SSIIM) are proven to be competent over the other existing works considered in this paper. In addition, both the proposed techniques have shown excellent anti-steganalytic ability against RS and pixel difference histogram (PDH) attack.
Reversible Image Steganography using dual-layer LSB matching
Source Title: Sensing and Imaging, Quartile: Q2
View abstract ⏷
Recently, reversible information hiding (RIH) methods have drawn substantial attention in many privacy-sensitive real-time applications, such as the Internet of Things (IoT) enabled communications, electronic health care infrastructure, and military applications. The RIH methods are proven to be competent in such hyper-sensitive infrastructures where the loss of a single bit of information is not acceptable. In this paper, dual-layered based RIH method using modified least significant bit (LSB) matching has been proposed. The objective of the proposed work is to enhance the embedding efficiency (EE) using dual-layer based embedding strategy and to curtail the distortion caused to the stego-image to improve its quality. At the first layer of embedding, each pixel conceals two bits of information using the proposed modified LSB matching method to produce the intermediate pixel pair (IPP). Further, the IPP is utilized to conceal four bits of information during the next layer of embedding. Experimental study reveals that, the proposed method can embed 1,572,864 bits of secret data with peak signal-to-noise ratio (PSNR) of 47.86 dB, 48.05 dB, 46.51 dB and 48.14 dB, for the respective images. Further, the image quality assessment parameters like structural similarity (SSIM) index and universal image quality index (Q) are as good as the existing literature. Additionally, the proposed method shows excellent anti-steganalysis ability to regular and singular (RS) and pixel difference histogram (PDH) analysis.
Digital Image Steganography using PVD and Modulo Operation
Source Title: Internetworking Indonesia Journal, Quartile: Q4
View abstract ⏷
This paper proposes an image steganographic approach using the principle of pixel value differencing (PVD) and modulo operation (MO). The major contributions of the proposed approach are: (i) increase in peak signal-to-noise ratio (PSNR), (ii) increase in hiding capacity, and (iii) avoidance of fall off boundary problem (FOBP). At first, the image is partitioned into non-overlapping blocks consisting of three consecutive pixels. Then, the secret data is embedded in a block using two phases, (i) pixel difference modulo operation (PDMO) phase, and (ii) average PVD (APVD) readjustment phase. In the first phase, the difference between two consecutive pixels of a block is found and using an adaptive range table and modulo operation the secret data are embedded. In the second phase, the average of the first two stego-pixels of the block and the third pixel is considered for data embedding using PVD approach. The result of the proposed approach has been compared with existing approaches and found to be improved.
A Novel n-Rightmost Bit Replacement Image Steganography Technique
Source Title: 3D Research,
View abstract ⏷
Image steganography is a technique for hiding the secret data in a carrier image. This paper proposes a novel n-right most bit replacement image steganography technique to hide the secret data in an image, where 1???n???4. The major objectives of the proposed technique are, (1) improving the peak signal to noise ratio (PSNR), (2) improving the embedding capacity (EC), (3) avoiding the fall of boundary problem (FOBP), and (4) robustness against salt and pepper noise and RS attack. Initially, the n-right most bits for each pixel and the n-bits of the secret data are converted to decimal values. Then, using the difference between these two decimal values the original pixels are readjusted to produce stego-pixels. From the experimental results it is observed that PSNR is higher for lower value of n and the EC is larger for the higher value of n. Furthermore, it is also experimentally investigated that the proposed technique is resistant to steganalytic attacks.
An optimal information hiding approach based on pixel value differencing and modulus function
Source Title: Wireless Personal Communications, Quartile: Q1
View abstract ⏷
This paper proposes an image steganography approach based on pixel value differencing and modulus function (PVDMF) to improve the peak signal-to-noise ratio (PSNR) and hiding capacity (HC). The proposed approach has two variants, (1) PVDMF 1 and (2) PVDMF 2. Both the variants use the difference between a pair of consecutive pixels to embed the secret data based on an adaptive range table. The modulus operations with pixel readjustment have been utilized to reduce the distortion in the stego-image. The experimental results prove that the PVDMF 1 offer higher PSNR and PVDMF 2 offers larger HC as compared to the existing approaches. In addition, the fall off boundary problem which exists in most of the pixel value differencing approaches has been avoided. Furthermore, it has been experimentally verified that the proposed approach is resistant against RS attack.
A Novel Multi Stego-Image based Data Hiding Method for Gray Scale Image
Source Title: Pertanika Journal of Science & Technology, Quartile: Q2
View abstract ⏷
In this paper, we present a novel multi stego-image based data hiding method using the principle of the modified least significant bit (LSB) matching to improve the embedding capacity (EC) as well as image quality. Initially, each original pixel produces four new pixels. The secret data is hidden in all the four produced pixels. Then the pixels are readjusted to improve the quality of the stego-images. There are four separate stego-images developed from the four different readjusted pixels. Each stego-image hides one bit per pixel. The average peak signal-to-noise ratios (PSNR) for the stego-images are 36.06 dB, 37.88 dB, 39.60 dB and 41.00 dB respectively. Furthermore, the proposed method successfully withstand against RS-steganalysis.
Pixel overlapping image steganography using PVD and modulus function
Source Title: 3D Research,
An improved data hiding technique using bit differencing and LSB matching.
Source Title: Internetworking Indonesia Journal, Quartile: Q4
Digital Image Steganography Using Bit Flipping
Dr Aditya Kumar Sahu, Dr Aditya Kumar Sahu, G Swain and E. Suresh Babu
Source Title: Cybernetics and Information Technologies, Quartile: Q2
View abstract ⏷
This article proposes bit flipping method to conceal secret data in the original image. Here a block consists of 2 pixels and thereby flipping one or two LSBs of the pixels to hide secret information in it. It exists in two variants. Variant-1 and Variant-2 both use 7th and 8th bit of a pixel to conceal the secret data. Variant-1 hides 3 bits per a pair of pixels and the Variant-2 hides 4 bits per a pair of pixels. Our proposed method notably raises the capacity as well as bits per pixel that can be hidden in the image compared to existing bit flipping method. The image steganographic parameters such as, Peak Signal to Noise Ratio (PSNR), hiding capacity, and the Quality Index (QI) of the proposed techniques has been compared with the results of the existing bit flipping technique and some of the state of art article.
Information hiding using group of bits substitution
Source Title: International Journal on Communications Antenna and Propagation, Quartile: Q2
View abstract ⏷
The importance of any steganographic approach is based on its capacity and security in data transmission. This article proposes an image steganographic method, called three group of bits substitution (3GBS) which hides 3 bits of secret data in a pixel. Each pixel of an image can hide 3 bits of secret data. The Peak Signal-to-Noise Ratio (PSNR) and hiding capacity are two important parameters to evaluate the strength of any steganographic method. This article compares the PSNR and hiding capacity of the proposed 3GBS method with the existing GBS methods. The experimental results of the proposed method gives a conclusive evidence that the hiding capacity increased significantly with an acceptable visual fidelity of the produced-image.
A review on LSB substitution and PVD based image steganography techniques.
Source Title: Indonesian Journal of Electrical Engineering and Computer Science,
View abstract ⏷
There has been a tremendous growth in Information and Communication technologies during the last decade. Internet has become the dominant media for data communication. But the secrecy of the data is to be taken care. Steganography is a technique for achieving secrecy for the data communicated in Internet. This paper presents a review of the steganography techniques based on least significant bit (LSB) substitution and pixel value differencing (PVD). The various techniques proposed in the literature are discussed and possible comparison is done along with their respective merits. The comparison parameters considered are,(i) hiding capacity,(ii) distortion measure,(iii) security, and (iv) computational complexity.
Performance evaluation parameters of image steganography techniques
Dr Aditya Kumar Sahu, Pradhan, A., Dr Aditya Kumar Sahu, Swain, G., & Sekhar, K. R
Source Title: In 2016 International conference on research advances in integrated navigation systems (RAINS),
View abstract ⏷
This paper illustrates the various performance evaluation parameters of image steganography techniques. The performance of a steganographic technique can be rated by three parameters; (i) hiding capacity, (ii) distortion measure and (iii) security. The hiding capacity means the maximum amount of information that can be hidden in an image. It can also be represented as the number of bits per pixel. The distortion is measured by using various metrics like mean square error, root mean square error, PSNR, quality index, correlation, structural similarity index etc. Each of these metrics can be represented mathematically. The security can be evaluated by testing the steganography technique with the steganalysis schemes like pixel difference histogram analysis, RS analysis etc. All these metrics are illustrated with mathematical equations. Finally, some future directions are also highlighted at the end of the paper.
