Gradient smoothing is an efficient approach to reducing noise in gradient-based model explanation methods. SmoothGrad adds Gaussian noise to mitigate much of this noise. However, the crucial hyperparameter in this method, the variance $\sigma$ of the Gaussian noise, is often set manually or determined using a heuristic approach. This results in the smoothed gradients containing extra noise introduced by the smoothing process. In this paper, we aim to analyze the noise and its connection to the out-of-range sampling in the smoothing process of SmoothGrad. Based on this insight, we propose AdaptGrad, an adaptive gradient smoothing method that controls out-of-range sampling to minimize noise. Comprehensive experiments, both qualitative and quantitative, demonstrate that AdaptGrad could effectively reduce almost all the noise in vanilla gradients compared to baseline methods. AdaptGrad is simple and universal, making it a practical solution to enhance gradient-based interpretability methods to achieve clearer visualization.

Time series anomaly detection is one kind of critical time series analytical tasks, which is widely applied to various real-world applications. Recently, the diffusion models have shown promising performance on time series imputation for anomaly detection. And the high computing requirements of the diffusion models naturally extend their computation paradigm from the centralized computing to the cloud computing, and then further to JointCloud computing which allows the diffusion models to be deployed across multiple clouds. However, the JointCloud computing paradigm faces the challenge of task offloading over multiple nodes across multiple clouds. Unfortunately, most of existing task offloading methods overlook the dynamic nature of network conditions among clouds. To address this issue, we propose a time series anomaly detection approach named AdaDiffAD by adaptively segmenting diffusion models in JointCloud environment with dynamic network conditions. Specifically, we design a task offloading strategy by segmenting the denoising process of the diffusion model onto both the edge clouds and central clouds, and utilizing the edge cloud results directly for anomaly detection when the network condition is not ideal. By conducting comprehensive experiments on seven datasets, the experimental results demonstrate that our proposed AdaDiffAD always achieves lower time consumption while maintaining competitive anomaly detection performance compared to the state-of-the art without adaptive task offloading strategy.

Anomaly detection in multivariate time series has emerged as a critical challenge in the time series research community with significant application potentials in various scenarios, ranging from fault diagnosis to system state estimation in Industrial Control Systems (ICSs). Meanwhile, the demand for high availability and extensibility of ICSs necessitates their deployment in the JointCloud environment. Therefore, the performance of the multivariate time series anomaly detection model is expected to be enhanced in the JointCloud environment when encountering dynamic network conditions among multiple clouds. Impressed by the effectiveness of diffusion models in anomaly detection, we have chosen diffusion models for empowering our anomaly detection model. Specifically, we propose Progressively Distilled Diffusion Anomaly Detection model (ProDiffAD) in the JointCloud environment to seek for the balance between effectiveness and efficiency. Moreover, our proposed model is capable of being adaptive with the dynamic network conditions in the JointCloud environment by modeling the intercloud network conditions. To validate the effectiveness and efficiency of our model, comprehensive experiments are conducted on two real and five synthetic datasets. The experimental results demonstrate that our proposed model achieves more accurate and faster multivariate time series anomaly detection in the JointCloud environment under dynamic network conditions compared to state-of-the-art models.

Deepfake threatens the authenticity of the information in artificial intelligence Internet of Things (IoT) systems. Recently, several deepfake detection methods have been proposed in academia and industry for securing the authenticity of visual information in the face of artificial intelligence advances. Frame-level detection methods, a widely employed security method against deepfake, have a small model size and offer real-time responsiveness, despite basing their classification decision only on the information contained within the frame they are evaluating. We propose a new lightweight frame-level detection technique based on Bayesian inference weighting (BIW) to improve the robustness of existing frame-level detection models. Our proposed BIW technique employs the Naive Bayesian algorithm to estimate the reliability of any candidate model’s detection results. Comprehensive experiments were conducted on the attacked data sets by four designed video interference approaches and edge computing platform, showing that BIW enhances the robustness of all the baselines and improves their detection accuracy with a real-time response.

In the field of data mining, Time Series Classification (TSC) has attracted lots of interest from researchers due to its wide range of applications. Recently, deep learning models have shown promising performance on TSC by automatically extracting discriminative features from original time series. However, deep learning models are very time-consuming for training and lack interpretability. In the contrast, shapelets-based algorithms are easy to be implemented with interpretability preserved. In this paper, we proposed a salience-guided shapelets selection approach named Points2Shapelets, which inherits the advantages of both deep learning-based models and shapelets-based algorithms for TSC. Specifically, an interpretable and transparent shapelets selection approach is designed by leveraging the salience analysis of the pre-trained model. By conducting comprehensive experiments on 20 public UCR time series datasets, the experimental results demonstrate that our proposed approach Points2Shapelets achieves competitive performance shown by deep learning-based models. Meanwhile, explanations about how the salience analysis guides the decision-making of TSC models are offered in a visually understandable manner.

In recent years, Convolutional Neural Networks (CNN s) have been widely applied in various applications due to its powerful learning capability. However, its lack of explainability hinders its further usage in tasks requiring high reliability. Therefore, interpretability technique is the key to the application and deployment of CNN models. As a typical interpretability technique for CNN, Class Activation Map (CAM) utilizing the gradient based weights and activation map is widely applied to traditional CNN models for offering visual interpretability. However, the activation map adopted by CAM cannot loyally quantify the relevance between input samples and activation values. Hence, in this paper, we propose a new interpretability approach called Salience-CAM employing salience scores to accurately measure the relevance between input samples and activation values. To evaluate the effectiveness of Salience-CAM, comprehensive experiments are conducted on 6 selected time series datasets. By leveraging an evaluation algorithm proposed in this paper, the experimental results show that our proposed Salience-CAM outperforms the baseline by discovering more discriminative features.