Deep neural networks (DNNs) have shown high accuracy in fault diagnosis, but they struggle to effectively capture changes over time in multivariate time-series data and suffer from resource consumption issues. Spike deep belief networks (spike-DBNs) address these limitations by capturing the change in time-varying signals and reducing resource consumption, but they sacrifice accuracy. To overcome these limitations, we propose integrating an event-driven approach into spike-DBNs through the Latency-Rate coding method and the reward-STDP learning rule. The encoding method enhances the event representation capability, while the learning rule focuses on the global behavior of spiking neurons triggered by events. Our proposed method not only maintains low resource consumption but also improves the fault diagnosis ability of spike-DBNs. We conducted a series of experiments to verify our model’s performance, and the results demonstrate that our proposed method improves the accuracy of fault classification of manipulators and reduces learning time by nearly 76% compared to spike-CNN under the same conditions. 

An event-driven Spike-DBN model for fault diagnosis using reward-STDP.

O-RAN is a new, open, adaptive, and intelligent RAN architecture. Motivated by the success of artificial intelligence in other domains, O-RAN strives to leverage machine learning (ML) to automatically and efficiently manage network resources in diverse use cases such as traffic steering, quality of experience prediction, and anomaly detection. Unfortunately, it has been shown that ML-based systems are vulnerable to an attack technique referred to as adversarial machine learning (AML). This special kind of attack has already been demonstrated in recent studies and in multiple domains. In this paper, we present a systematic AML threat analysis for O-RAN. We start by reviewing relevant ML use cases and analyzing the different ML workflow deployment scenarios in O-RAN. Then, we define the threat model, identifying potential adversaries, enumerating their adversarial capabilities, and analyzing their main goals. Next, we explore the various AML threats associated with O-RAN and review a large number of attacks that can be performed to realize these threats and demonstrate an AML attack on a traffic steering model. In addition, we analyze and propose various AML countermeasures for mitigating the identified threats. Finally, based on the identified AML threats and countermeasures, we present a methodology and a tool for performing risk assessment for AML attacks for a specific ML use case in O-RAN.

Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)

Multi-focus image fusion aims to create an all-in-focus clear image by fusing a set of partially focused images. In recent years, various multi-focus image fusion methods based on deep learning have been proposed, but there is no thorough study evaluating their robustness for adversarial attacks. In this paper, we investigate the robustness of deep learning based multi-focus image fusion models to adversarial attacks. First, we generated adversarial examples which can significantly reduce the fusion quality of image fusion models. Then, a metric defocus attack intensity (DAI) was proposed to quantitatively evaluate the robustness of different models for adversarial attacks. At last, we analyzed the factors affecting model robustness, including model size and post-processing steps. Besides, we successfully attacked recent image fusion models in the black-box scene by utilizing the transferability of adversarial examples. Experimental results show that state-of-the-art image fusion models are also vulnerable to adversarial attacks, and some observations in image classifier robustness studies are not transferable to image fusion task. 

Adversarial Attacks on Multi-focus Image Fusion Models

A method for recovering adversarial samples with both adversarial attack forensics and recognition accuracy

After generation units are connected to the power grid, electrical parameters such as generation unit output voltage, current and power need to be monitored for the safe and reliable function of grids. Obtaining accurate parameters of synchronous generation units (SGUs) is normally the basic requirement for grid stability analysis and control. To improve the accuracy of identifying SGU parameters, this study proposes graph attention-based U-Net conditional generative adversarial networks (GAUCGANs) for the identification of SGU parameters. The mapping between real and generated samples is modeled by applying conditional generative adversarial networks (CGANs). The CGANs of the GAUCGANs proposed in this study consist of U-Nets-based generators, fully convolutional networks-based discriminators, and graph attention networks (GATs). The U-Nets of the GAUCGANs enhance the ability of generators to generate generative samples that are infinitely close to real samples. The U-Nets-based generators of the GAUCGANs are responsible for generating the SGU parameters; the fully convolutional networks-based discriminators of the GAUCGANs determine whether data is from the generator or the real data. In addition, the classification loss and sample labels are derived from the GATs. The addition of GATs will further enhance the ability of the generators to remove adversarial disturbances. In this study, the proposed GAUCGAN for parameter identification is compared with the traditional parameter identification algorithms. The comparison algorithms are applied to the Simulink standard SGU, hydroelectric unit No. 6 of the waterfall gorge hydropower plant, and the generation unit at the 18th bus of the IEEE 36-bus power system. The numerical simulation results from the GAUCGANs verify that the GAUCGAN can more accurately identify SGU parameters than the traditional parameter identification algorithms. The GAUCGANs improve the accuracy by at least 61.60% on average over the other methods by comparing the RMSE, MAE, MAPE, and SMAPE under three cases comprehensively. 

Graph attention-based U-net conditional generative adversarial networks for the identification of synchronous generation unit parameters

A novel and universal GAN-based countermeasure to recover adversarial examples to benign examples

In this work we build an end-to-end adversarial domain adaptation model for bearing fault diagnosis on two different datasets. Two different adversarial based unsupervised domain adaptation models are implemented, and the obtained results are compared and analyzed. This project proposed a novel feature extractor model structure for bearing vibration signal, and pseudo-label semi-supervised learning is applied with the implemented Maximum Classifier Discrepancy (MCD) model. The proposed method outperforms the original method on XJTU and CWRU bearing datasets and achieves 97.25% accuracy. The codes are available at https://github.com/Leolando/FYP.

Adversarial Based Unsupervised Domain Adaptation for Bearing Fault Diagnosis

This paper contributes to improving a bottleneck residual block-based feature extractor as a set of layers for transforming raw data into features for classification. This structure is utilized to avoid the issues of the deep learning network, such as overfitting problems and low computational efficiency caused by redundant computation, high dimensionality, and gradient vanishing. With this structure, a domain adversarial neural network (DANN), a domain adversarial unsupervised model, and a maximum classifier discrepancy (MCD), a domain adaptation model, have been applied to conduct a binary classification of fault diagnosis data. In addition, a pseudo-label is applied to MCD for comparison with the original one. In comparison, several popular models are selected for transferability estimation and analysis. The experimental results have shown that DANN and MCD with this improved feature extractor have achieved high classification accuracy, with 96.84% and 100%, respectively. Meanwhile, after using the pseudo-label semi-supervised learning, the average classification accuracy of the MCD model increased by 15%, increasing to 94.19%.

/pdf/unsupervised-method-based-on-adversarial-domain-adaptation-1k8pxb3x.pdf

Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis

This paper focuses on an important type of black-box attacks, i.e., transfer-based adversarial attacks, where the adversary generates adversarial examples by a substitute (source) model and utilize them to attack an unseen target model, without knowing its information. Existing methods tend to give unsatisfactory adversarial transferability when the source and target models are from different types of DNN architectures (e.g. ResNet-18 and Swin Transformer). In this paper, we observe that the above phenomenon is induced by the output inconsistency problem. To alleviate this problem while effectively utilizing the existing DNN models, we propose a common knowledge learning (CKL) framework to learn better network weights to generate adversarial examples with better transferability, under fixed network architectures. Specifically, to reduce the model-specific features and obtain better output distributions, we construct a multi-teacher framework, where the knowledge is distilled from different teacher architectures into one student network. By considering that the gradient of input is usually utilized to generated adversarial examples, we impose constraints on the gradients between the student and teacher models, to further alleviate the output inconsistency problem and enhance the adversarial transferability. Extensive experiments demonstrate that our proposed work can significantly improve the adversarial transferability.

pdf/common-knowledge-learning-for-generating-transferable-2mbwe2bk.pdf

Common Knowledge Learning for Generating Transferable Adversarial Examples

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Papers

A novel and universal GAN-based countermeasure to recover adversarial examples to benign examples

Adversarial Based Unsupervised Domain Adaptation for Bearing Fault Diagnosis

Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis

Common Knowledge Learning for Generating Transferable Adversarial Examples