scholarly journals LSB steganography detection in monochromatic still images using artificial neural networks

2021 ◽  
Author(s):  
Julián D. Miranda ◽  
Diego J. Parada
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Binary Classification ◽  
Genome Project ◽  
Classification Model ◽  
Still Images ◽  
Standard Deviation Range ◽  
Artificial Neural ◽  
Image Pairs ◽  
Graphic Content

AbstractEmbedding graphic content in multimedia through steganography is a useful and fast practice to hide information. However, detecting the use of this technique is complex and sometimes unsuccessful because variations are not visually perceptible. This article proposes the use of a binary classification model based on artificial neural networks to detect the presence of LSB steganography on monochromatic still images of 256x256 and 8 bits, based on the Standford Genome Project. The steganograms were generated by varying the payload from 0.1 to 0.5 to obtain image pairs of carriers and steganograms. For each steganogram, the following features were extracted from image histograms: kurtosis, skewness, standard deviation, range, median, harmonic mean, Hjorth mobility, and complexity. The results show that the classifier reaches a 91.45% accuracy in detecting LSB steganography when learning from all payloads, as well as a 96.78% individual classification accuracy in the best case with a payload of 0.5.

scholarly journals A Semi-supervised Learning-Based Diagnostic Classification Method Using Artificial Neural Networks

2021 ◽  
Vol 11 ◽  
Author(s):  
Kang Xue ◽  
Laine P. Bradshaw
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Supervised Learning ◽  
Real Data ◽  
Classification Model ◽  
Diagnostic Classification ◽  
Diagnostic Quality ◽  
Q Matrix ◽  
Artificial Neural ◽  
Research Studies

The purpose of cognitive diagnostic modeling (CDM) is to classify students' latent attribute profiles using their responses to the diagnostic assessment. In recent years, each diagnostic classification model (DCM) makes different assumptions about the relationship between a student's response pattern and attribute profile. The previous research studies showed that the inappropriate DCMs and inaccurate Q-matrix impact diagnostic classification accuracy. Artificial Neural Networks (ANNs) have been proposed as a promising approach to convert a pattern of item responses into a diagnostic classification in some research studies. However, the ANNs methods produced very unstable and unappreciated estimation unless a great deal of care was taken. In this research, we combined ANNs with two typical DCMs, the deterministic-input, noisy, “and” gate (DINA) model and the deterministic-inputs, noisy, “or” gate (DINO) model, within a semi-supervised learning framework to achieve a robust and accurate classification. In both simulated study and real data study, the experimental results showed that the proposed method could achieve appreciated performance across different test conditions, especially when the diagnostic quality of assessment was not high and the Q-matrix contained misspecified elements. This research study is the first time of applying the thinking of semi-supervised learning into CDM. Also, we used the validating test to choose the appropriate parameters for the ANNs instead of using typical statistical criteria.

scholarly journals Use of artificial neural networks in biosensor signal classification

2008 ◽  
Vol 56 (2) ◽  
pp. 73-80
Author(s):  
Vlastimil Dohnal ◽  
Lenka Podloucká ◽  
Zuzana Grosmanová ◽  
Jiří Krejčí
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Method ◽  
Classification Model ◽  
Signal Classification ◽  
Composition Analysis ◽  
Chemical Information ◽  
Measured Signal ◽  
Artificial Neural ◽  
Substrate Addition

Biosensors are analytical devices that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytical signal and that utilizes a biochemical mechanism for the chemical recognition. The complexity of biosensor construction and generation of measured signal requires the development of new method for signal eva­luation and its possible defects recognition. A new method based on artificial neural networks (ANN) was developed for recognition of characteristic behavior of signals joined with malfunction of sensor. New algorithm uses unsupervised Kohonen self-organizing neural networks. The work with ANN has two phases – adaptation and prediction. During the adaptation step the classification model is build. Measured data form groups after projection into two-dimensional space based on theirs similarity. After identification of these groups and establishing the connection with signal disorders ANN can be used for evaluation of newly measured signals. This algorithm was successfully applied for 540 signal classification obtained from immobilized acetylcholinesterase biosensor measurement of organophosphate and carbamate pesticides in vegetables, fruits, spices, potatoes and soil samples. From six different signal defects were successfully classified four – low response after substrate addition, equilibration at high values, slow equilibration after substrate addition respectively low sensitivity on syntostigmine.

scholarly journals Methodology for Knowledge Extraction from Trained Artificial Neural Networks

2018 ◽  
Vol 21 ◽  
pp. 6-14
Author(s):  
Andrey Bondarenko ◽  
Ludmila Aleksejeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Binary Classification ◽  
Knowledge Extraction ◽  
Classification Problems ◽  
Critical Areas ◽  
Artificial Neural ◽  
Fully Connected

Artificial neural networks are widely spread models that outperform more basic, but explainable machine learning models like classification decision tree. Although their lack of explainability severely limits their area of application. All mission critical areas or law regulated areas (like European GDPR) require model to be explained. Explainability allows model validation for correctness and lack of bias. Thus methods for knowledge extraction from artificial neural networks have gained attention and development efforts. Current paper addresses this problem and describes knowledge extraction methodology which can be applied to classification problems. It is based on previous research and allows knowledge to be extracted from trained fully connected feed-forward artificial neural network, from radial basis function neural network and from hyper-polytope based classifier in the form of binary classification decision tree, elliptical rules and If-Then rules.

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