Optimal and Linear F-Measure Classifiers Applied to Non-technical Losses Detection

Rodríguez, Felipe; Martino, Matías Di; Kosut, Juan Pablo; Santomauro, Fernando; Lecumberry, Federico; Fernández, Alicia

doi:10.1007/978-3-319-25751-8_11

Cited by 4 publications

(2 citation statements)

References 26 publications

(32 reference statements)

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“…In this paper, we applied both SPAM -Subtractive Pixel Adjacency Model [22] and CC-PEV [23][24] that are used to produce and extract the features for steganalysis. The length of SPAM 686 features consequently with the one extra feature called class in case of labeling the instances which including two types, the former is class "1" stands for cover images and the latter is class "2" stands for stego images.…”

Section: The Feature Extractorsmentioning

confidence: 99%

Region based Image Steganalysis using Artificial Bee Colony

Mohammadi

Sajedi

2017

Journal of Visual Communication and Image Representation

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Steganalysis is the art and skill of discriminating stego images from cover images. Image steganalysis algorithms can be divided into two broad categories, specific and universal. In this paper, a novel universal image steganalysis algorithm is proposed which is called RISAB, Region based Image steganalysis using Artificial Bee colony. The goal of the proposed method is to realize a sub-image from stego and cover images through ABC with respect to density according to the cover, stego and difference images. In our method, we look for the best sub-image, which contains the highest density with respect to the changed embedding pixels. Furthermore, after selecting the best sub-image, we extract the features, which have been selected by IFAB, Image steganalysis based on Feature selection using Artificial Bee colony. At the end, both selected features by IFAB and extracted features by RISAB are combined. As a result, a feature vector is generated which improves accuracy of steganalysis. Experimental results show that our proposed method outperforms other approaches.

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Section: The Feature Extractorsmentioning

confidence: 99%

Region based Image Steganalysis using Artificial Bee Colony

Mohammadi

Sajedi

2017

Journal of Visual Communication and Image Representation

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“…To address the issue of imbalanced data, researchers such as Zanetti et al [12], Rodriguez et al [13], and Martino et al [14] have proposed using one-class Support Vector Machines (SVM) for electricity theft detection. Unlike the classical SVM model that requires two classes of training samples (normal and abnormal), a one-class SVM only requires a specific course of training samples (in our case, using standard consumption patterns).…”

mentioning

confidence: 99%

Dynamic Generative Residual Graph Convolutional Neural Networks for Electricity Theft Detection

Zhuang,

Jiang,

Xia

et al. 2024

IEEE Access

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Driven by economic incentives, illegal electricity consumers pose significant threats to the economic and security aspects of the power system by illicitly accessing or manipulating electrical resources. With the widespread adoption of Advanced Metering Infrastructure (AMI), researchers have turned to leveraging smart meter data for electricity theft detection. However, existing models rely on methods that model a single electricity load curve and cannot capture the temporal dependencies, periodicity, and underlying features between electricity consumption cycles. This paper proposes a dynamic generation algorithm to address these issues to construct a topological structure between periodic nodes, updated during training. Subsequently, residual graph convolution operations extract temporal and spatial dependencies among nodes. Additionally, to address the issue of model instability caused by scarce theft data, we employ the SMOTE (Synthetic Minority Over-sampling Technique) oversampling technique and enhance overall classification performance by modifying class weights in the loss function. We trained this network architecture on the real SGCC (State Grid Corporation of China) dataset, and the results demonstrate its superiority over other mainstream existing models.

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Adaptive Image Steganalysis

Sajedi

2017

Multimed Tools Appl

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Optimal and Linear F-Measure Classifiers Applied to Non-technical Losses Detection

Cited by 4 publications

References 26 publications

Region based Image Steganalysis using Artificial Bee Colony

Region based Image Steganalysis using Artificial Bee Colony

Dynamic Generative Residual Graph Convolutional Neural Networks for Electricity Theft Detection

Adaptive Image Steganalysis

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