Bio inspired optimization for universal spatial image steganalysis

Acquiring the best image features that best distinguishes a stego and clean image is a challenge in image steganalysis. Though higher order models acquire all these features, they pose problems due to computational complexity in terms of time and space. This demands optimization of the feature sets. Compared to the existing statistical feature optimization techniques, genetic algorithm based optimization techniques are evolving to be more promising. The existing deterministic methods of optimization have the limitation of converging into local minima as compared to the evolutionary methods which tend to converge to the global minima. Objectives: This paper intends to review the various genetic algorithm based feature optimization techniques applicable for image steganalysis of JPEG images and identify the best algorithm that converges to global minima. Method/Analysis: The methods analysed include the stochastic (metaheuristic) algorithms that make use of the random behaviour of plants and animals. The Antlion behaviour based optimization technique (ALO) has been implemented and analysed for JPEG stego images. The movement of ants are modelled as random walk and the traps built by antlions are assumed proportional to their fitness. The antlions shoot sand outwards to pull the ants inside the pits. This causes sliding down of the ants into the pits to the most minimum position. The coding of the optimization is implemented in Matlab with images taken from the standard BOSS database. Findings: The feature set after feature extraction has a dimension of 2000 × 48600 with 1000 cover and 1000 clean images. Considering these vectors as the initial positions of the ants in the Ant Lion Optimizer, for a payload of 0.5 in embedding logic the classification accuracies are studied. The convergence of this optimizer is proved according to the convergence curve for 300 iterations. After optimization, the reduced feature set is used to classify the image as cover or stego image. SVM, MLP and the fusion classifiers - Bayes, Decision template and Dempster Schafer are used. For low levels of embedding changes, the classification by MLP and Fusion schemes is good. For medium and high levels of embedding changes, the classification by Fusion schemes alone is good. It has been identified that the proposed steganalyser gives best results for Bayes fusion classification (69%) scheme when Antlion behaviour is used as optimizer. Applications/Improvements: This research has implemented a novel method of image feature optimization that improves steganalysis. The optimized feature set is 100 times less in dimension assuring reduced computational complexity in time and space. Improved version of this research may include a different selection mechanism or using a different optimization function.

The performance accuracy of JPEG steganalysis depends on the best features extracted from the images. This demands extraction of all possible features that undergo changes during embedding. The computational complexity due to such large number of features necessitates feature set optimization. Existing research in JPEG image steganalysis tend to extract rich feature sets and reduce them by statistical feature reduction techniques. Compared to these techniques, genetic algorithm based optimization techniques are more promising as they converge to global minima. The objective of this paper is to implement genetic based optimization to reduce the high dimensional image features and hence obtain improved classification accuracy. The method implemented includes the extraction of image features in terms of co-occurrence matrices of the differences of all possible Discrete Cosine Transform (DCT) coefficients to give 200 × 23,230 features. These features are optimized by a nature inspired meta-heuristic, Ant Lion Optimization (ALO) which considers the features as ants that move in random space. The fitness function for the antlion to hunt the ants is proportional to the traps built by the antlion. The proposed steganalyser has been tested for classification accuracies with different payloads. The classifiers implemented include Support Vector Machines (SVM), Multi Layer Perceptron (MLP) and fusion classifiers based on Bayes, Decision template and Dempster Schafer data fusion schemes. The results show that highest average classification accuracy has been obtained for Bayes fusion classifier followed by Dempster Schafer fusion classifier. It has been noted that the performance of fusion classifiers is better compared to individual classifiers. Thus the proposed method gives better classification accuracy for JPEG steganalysis than existing methods.

Blind universal steganalysis has been the choice of Steganalysers owing to it’s capability to detect stego images without any prior information about the embedding method. Universal steganalysis is a two class optimization problem and the detecting efficiency depends on the feature set chosen from the stego and clean images. Though extracting all possible features of an image may lead to more efficiency the classification suffers due to large dimension of feature set. To overcome the problem of dimensionality appropriate feature reduction techniques need to be employed. This paper presents a blind universal image steganalysis technique that extracts the noise models of adjacent pixels of an image. The exact model construction involves the formation of four dimensional co-occurrence matrices of the quantised and truncated noise residues. From the 106 sub models 34,671 features have been extracted and further reduced by a novel unsupervised optimization technique to identify the most appropriate features for classification. The classifiers implemented include Support Vector Machines (SVM), Multi Layer Perceptron (MLP) and three fusion classifiers based on Bayes, Decision Template and Dempster Schafer fusion schemes. It has been identified that MLP performs better than SVM but is not superior to fusion classifiers. Comparing all the classifiers, Decision Template based fusion method gives the best classification accuracy (99.25%). Thus the proposed unsupervised optimization method combined with Decision Template fusion classification scheme provides the best classification of stego and clear images as compared to the existing research work.

Image steganalysis involves the discovery of secret information embedded in an image. The common method is blind image steganalysis, which is a two‐class classification problem. Blind steganalysis extracts all possible feature variations in an image due to embedding, select the most appropriate feature data, and then classifies the image. The dimensionality of the extracted image features are high and demand data reduction to identify the most relevant features and to aid accurate classification of an image. The classification is under two classes namely, clean (cover) image and stego (image with embedded secret data) image. Since the classification accuracy depends on selection of most appropriate features, opting for the best data reduction or data optimization algorithms becomes a prime requisite. Research shows that most of the statistical optimization techniques converge to local minima and lead to less classification accuracy as compared to bio‐inspired methods. Bio‐inspired optimization methods obtain improved classification accuracy by reducing the high‐dimensional image features. These methods start with an initial population and then optimize them in steps till a global optimal point is reached. Examples of such methods include Ant Lion Optimization (ALO), Fire Fly Algorithm (FFA), and literature shows around 54 such algorithms. Bio‐inspired optimization has been applied in various fields of design optimization and is novel to image steganalysis. This article analyses the various bio‐inspired optimization techniques and their accuracy in image steganalysis pertaining to the discovery of embedded information in both JPEG and spatial domain steganalysis.This article is categorized under: Technologies > Classification Technologies > Computational Intelligence Technologies > Artificial Intelligence

In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.

In the paper, a new universal image steganalysis system based on wavelet packet decomposition (WPD) and empirical transition matrix in wavelet domain is proposed. First, it decomposes the test image using two-level Haar WPD. The statistical analysis is made both for the test image and their wavelet packet subbands. Second, it exploits the interscale and intrascale dependencies between wavelet coefficients. Markov empirical transition matrices are used to capture these dependencies. Fisher linear discriminator (FLD) is applied as classifier to distinguish between cover images and stego images. The experimental results have demonstrated that the proposed scheme has outperformed the existing steganalyzers in attacking a wide range of steganographic schemes include SSIS (Spread spectrum image steganography), Hide4PGP, Jsteg, F5, OutGuess.

Digital audio provides a suitable cover for high-throughput steganography. At 16 bits per sample and sampled at a rate of 44,100 Hz, digital audio has the bit-rate to support large messages. In addition, audio is often transient and unpredictable, facilitating the hiding of messages. Using an approach similar to our universal image steganalysis, we show that hidden messages alter the underlying statistics of audio signals. Our statistical model begins by building a linear basis that captures certain statistical properties of audio signals. A low-dimensional statistical feature vector is extracted from this basis representation and used by a non-linear support vector machine for classification. We show the efficacy of this approach on LSB embedding and Hide4PGP. While no explicit assumptions about the content of the audio are made, our technique has been developed and tested on high-quality recorded speech.

Extended decision template presentation for combining classifiers

This paper deals with insulating part quality control according to image analysis. The studied insulating parts are mainly composed of glass fibres and their orientation is directly correlated to the quality of the parts. This complex phenomenon is analyzed by means of 3D-tomographic images which give a huge set of raw data. Relevant features were extracted by several classification approaches to detect interesting regions defined by experts. The paper focuses on a fusion system based on decision templates to aggregate the previously obtained image classifications. The initial decision templates method proposed by L. Kuncheva is adapted for the image analysis concerned: (1) a solution is proposed to take into account classes of rejects for which there are no reference regions to learn the corresponding decision templates and (2) neighboring pixels are considered inside the fusion process of the decision templates. The fusion approach is then applied to part quality control. Results are assessed by means of the confusion matrix and accuracy measures show the great improvement in region detection brought by the fusion approach according to each input classification.

Statistics from reputable sources, including the World Health Organization (WHO), demonstrate that cancer is a leading cause of death globally, accounting for millions of deaths each year. When it comes to the early identification of cancer, machine learning (ML) is crucial. To analyze complex data and identify minute patterns that may indicate the presence of cancer, it employs robust computational approaches. Improving patient outcomes relies on early cancer detection since it paves the way for faster treatment and intervention, which might lead to better prognoses and higher survival rates. To choose features, this study intends to build an ML-based ensemble model utilizing ant colony optimization (ACO) and ant lion optimization (ALO). Next, ML classifiers are used as the initial predictions' basis learners. The last forecast is the result of combining two ensemble methods: voting and averaging classifiers. Four distinct cancer microarray datasets are used to assess the approach. With an accuracy of 99.08% on the Lung cancer dataset, the voting ensemble classifier outperforms the others, according to the empirical analysis.

Orthogonal frequency-division multiplexing [OFDM] is an information transfer technique in which a single data flow is divided between several closely spaced narrowband subchannel frequency range rather than a single Wideband channel frequency. The information is sent to the relay node there is a delay and some data is lost in the relay node is the major issue in the existing system. To overcome these challenges, The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. In this paper, a novel Five Input Hybrid Optimization Relay Node Selection and Energy Efficient Routing (FIHORNSEER) technique has been proposed for choosing the best relay based on noises. Ant Lion Optimization (ALO) is initially utilized to select the relay node's elite position. Secondly, the Crow Search Optimization (CSO) Algorithm is used for the phenomenon of position and memory of each relay. Finally, the Memetic Algorithm (MA) was generated by integrating the Ant Lion and Crow search optimization algorithm for the best relay node selection. The proposed framework is compared with previous techniques like FRNSEER, LMMSE, and HABO-OFDM Methods in terms of performance analysis, such as average utility, Energy Consumption, and Network Life Time. The result shows that the proposed FIHORNSEER improves the energy consumption better than 22.01%, 16.4%, and 12.2% FRNSEER, LMMSE, and HABO-OFDM, respectively.

Purpose The miniscule wireless sensor nodes, engaged in the wide range of applications for its capability of monitoring the physical changes around, requires an improved routing strategy with the befitting sensor node arrangement that plays a vital part in ensuring a completeness of the network coverage. Design/methodology/approach This paves way for the reduced energy consumption, the enhanced network connections and network longevity. The conventional methods and the evolutionary algorithms developed for arranging of the node ended with the less effectiveness and early convergence with the local optimum respectively. Findings The paper puts forward the befitting arrangement of the sensor nodes, cluster-head selection and the delayless routing using the ant lion (A-L) optimizer to achieve the substantial coverage, connection, the network-longevity and minimized energy consumption. Originality/value The further performance analysis of the proposed system is carried out with the simulation using the network simulator-2 and compared with the genetic algorithm and the particle swarm optimization algorithm to substantiate the competence of the proposed routing method using the ant lion optimization.

Marine sediments record much information of the ecological process, which highly correlated with global and local environmental change. Particle size and its distributions of sediments indicate different ecological functions, thus are the key questions in marine ecology. The analysis method is tedious and laborious, which is not conducive for in-situ monitoring. Here, a spectral analysis was explored using surface sediments sampled in the intertidal zone of Dongdayang village, Qingdao, China. These samples were dried and sieved to pass through the mesh size of 0.3 mm, 0.2 mm, 0.1 mm, and 0.075 mm, respectively. Then, four types of subsamples were collected with the particle size of 0.3-0.2 mm, 0.2-0.1 mm, 0.1-0.075 mm, and <; 0.075 mm, respectively. The visible and near infrared reflectance spectra (226-975nm) of these subsamples with different particle size were measured. Results showed that there was a negative correlation between the spectral reflectance and the particle size. And the characteristic spectra for particle size classification were 926-975nm. These particle size were classified by the support vector machine algorithm. The classification accuracy for the calibration set and validation set was 100% and 89.06%, respectively. Furthermore, the fusion classifier was compared with the single classifier. Three spectral bands were selected as the single particle size classifier, that is 226-325nm, 826-925nm and 226-975nm. These three single classifiers were fused by voting method, forming multiple classifier fusion. A fusion classifier was recommended whose validation set had the classification accuracy of 93.75%, better than any single classifier. Multiple classifier fusion is a good tool for searching the characteristic spectra of the chemical and physical parameter in sediments. This method provides a solution for the division of particle size in sediment.

Score level fusion of classifiers in off-line signature verification

Multi-scale image decomposition is crucial for image fusion, extracting prominent feature textures from infrared and visible light images to obtain clear fused images with more textures. This paper proposes a fusion method of infrared and visible light images based on spatial domain and image features to obtain high-resolution and texture-rich images. First, an efficient hierarchical image clustering algorithm based on superpixel fast pixel clustering directly performs multi-scale decomposition of each source image in the spatial domain and obtains high-frequency, medium-frequency, and low-frequency layers to extract the maximum and minimum values of each source image combined images. Then, using the attribute parameters of each layer as fusion weights, high-definition fusion images are through adaptive feature fusion. Besides, the proposed algorithm performs multi-scale decomposition of the image in the spatial frequency domain to solve the information loss problem caused by the conversion process between the spatial frequency and frequency domains in the traditional extraction of image features in the frequency domain. Eight image quality indicators are compared with other fusion algorithms. Experimental results show that this method outperforms other comparative methods in both subjective and objective measures. Furthermore, the algorithm has high definition and rich textures.