Jingsheng Lei

Steganalysis Over Large-Scale Social Networks With High-Order Joint Features and Clustering Ensembles

This paper tackles a recent challenge in identifying culprit actors, who try to hide confidential payload with steganography, among many innocent actors in social media networks. The problem is called steganographer detection problem and is significantly different from the traditional stego detection problem that classifies an individual object as a cover or a stego. To solve the steganographer detection problem over large-scale social media networks, this paper proposes a method that uses high-order joint features and clustering ensembles. It employs 250-D features calculated from the high-order joint matrices of Discrete Cosine Transform (DCT) coefficients of JPEG images, which indicate the dependencies of image content. Furthermore, a number of hierarchical sub-clusterings trained by the features are integrated as a clustering ensemble based on the majority voting strategy, which is used to make optimal decisions on suspicious steganographers. Experimental results show that the proposed scheme is effective and efficient in identifying potential steganographers in large-scale social media networks, and has better performance when tested against the state-of-the-art steganographic methods.

From Landscape to Portrait: A New Approach for Outlier Detection in Load Curve Data

In power systems, load curve data is one of the most important datasets that are collected and retained by utilities. The quality of load curve data, however, is hard to guarantee since the data is subject to communication losses, meter malfunctions, and many other impacts. In this paper, a new approach to analyzing load curve data is presented. The method adopts a new view, termed portrait, on the load curve data by analyzing the periodic patterns in the data and reorganizing the data for ease of analysis. Furthermore, we introduce algorithms to build the virtual portrait load curve data, and demonstrate its application on load curve data cleansing. Compared to existing regression-based methods, our method is much faster and more accurate for both small-scale and large-scale real-world datasets.

A Distributed and Scalable Approach to Semi-Intrusive Load Monitoring

Non-intrusive appliance load monitoring (NIALM) helps identify major energy guzzlers in a building without introducing extra metering cost. It motivates users to take proper actions for energy saving and greatly facilitates demand response (DR) programs. Nevertheless, NIALM of large-scale appliances is still an open challenge. To pursue a scalable solution to energy monitoring for contemporary large-scale appliance groups, we propose a distributed metering platform and use parallel optimization for semi-intrusive appliance load monitoring (SIALM). Based on a simple power model, a sparse switching event recovering (SSER) model is established to recover appliance states from their aggregated load data. Furthermore, the sufficient conditions for unambiguous state recovery of multiple appliances are presented. By considering these conditions as well as the electrical network topology constraint, a minimum number of meters are obtained to correctly recover the energy consumption of individual appliances. We evaluate the performance of both SIALM and NIALM with real-world trace data and synthetic data. The results demonstrate that with the help of a small number of meters, the SIALM approach significantly improves the accuracy of energy disaggregation for large-scale appliances.

EAPA: An efficient authentication protocol against pollution attack for smart grid

The development of smart grid depends on increased deployment of two-way communication to improve its efficiency and reliability of power delivery. However, this additional dependency also expands the risk from pollution attacks, which consist of injecting malicious packets in the network. The pollution attacks are amplified by the network coding process, resulting in a greater damage to the state estimation and decision making. In this paper, we address this issue by designing an efficient authentication protocol, named EAPA, for the smart grid communication. The proposed scheme allows not only recipient nodes, but also intermediate nodes, to verify the integrity and origin of the packets received without having to decode, and thus detect and discard the malicious packets in transit that fail the verification. By this means, the pollution is canceled out before reaching the destinations. Security analysis demonstrates that the EAPA can be resilient to data/tag pollution attacks and replay attacks. Performance evaluation results show that our EAPA can reduce much more communication overhead than Wu’s scheme.

Efficient steganographer detection over social networks with sampling reconstruction

This work proposes an improvement solution in identifying malicious user (or steganographer) who try to deliver hidden information in a batch of natural images. In this solution, a sampling construction strategy is proposed firstly. We design a probability calculation model by analying the principle of adaptive steganography, and then select DCT blocks with higher embedding probability to reconstruct a sample image, which is considered as the proof of extracting steganalysis features. Furthermore, inspired by the classical PEV-193 feature space, we reform a reduced PEV feature set including histogram features and intra-block co-occurrence features, which can capture more steganographic changes and match the sampling construction strategy well. Comprehensive experimental results show that comparing with the state-of-the-arts, the proposed scheme has a significant improvement in identifying potential steganographers in large-scale social media networks, and therefore is believed to be able to resist adaptive steganography with small payload.

Unsupervised steganalysis over social networks based on multi-reference sub-image sets

This work proposes a new unsupervised steganalysis scheme which mainly tackles the challenge in identifying individual JPEG image as stego or cover. The proposed scheme does not need a large number of samples to train classification model, and thus it is significantly different from the existing supervised steganalysis schemes. The proposed scheme employs calibration technology to construct multiple reference images from one suspicious image. These reference images are considered as the imitation of cover. Furthermore, randomized sampling is performed to construct sub-image sets from suspicious image and reference images, respectively. By calculating the maximum mean discrepancy between any two sub-image sets, an efficient measure is provided to give the optimal decision on this suspicious image. Experimental results show that the proposed scheme is effective and efficient in identifying individual image, and outperforms the state-of-the-art steganalysis scheme.

SHARK: sparse human action recovery with knowledge of appliances and load curve data

Occupancy detection can greatly facilitate heating, ventilation and cooling and lightning control for building energy saving. Sensor-based occupancy detection is usually costly and may suffer from high false positive rates. As such, occupancy detection using load curve data has been proposed. Such methods, however, normally (i) rely on tedious and nontrivial model training process and (ii) do not consider the influence of corrupted data in load curve. To overcome these pitfalls, we develop a practical, robust non-intrusive occupancy detection approach that does not require model training and data cleansing. Only using load curve data and readily available appliance knowledge, the method achieves occupancy detection by three main steps: (i) the appliances’ mode states are firstly decoded via a carefully designed robust sparse switching event recovering model; (ii) the human actions are recovered with a priori knowledge of human-activated switching events; (iii) the occupancy states are then inferred based on the recovered human actions along with empirical strategies and association rules. We evaluate our approach and compare it with existing methods with real-world data. The results show that our approach can achieve similar performance to those using supervised machine learning.

Multi-Source Stego Detection with Low-Dimensional Textural Feature and Clustering Ensembles

This work tackles a recent challenge in digital image processing: how to identify the steganographic images from a steganographer, who is unknown among multiple innocent actors. The method does not need a large number of samples to train classification model, and thus it is significantly different from the traditional steganalysis. The proposed scheme consists of textural features and clustering ensembles. Local ternary patterns (LTP) are employed to design low-dimensional textural features which are considered to be more sensitive to steganographic changes in texture regions of image. Furthermore, we use the extracted low-dimensional textural features to train a number of hierarchical clustering results, which are integrated as an ensemble based on the majority voting strategy. Finally, the ensemble is used to make optimal decision for suspected image. Extensive experiments show that the proposed scheme is effective and efficient and outperforms the state-of-the-art steganalysis methods with an average gain from 4 % to 6 % .