Deep neural networks have recently achieved notable progress in 3D point cloud recognition, yet their vulnerability to adversarial perturbations poses critical security challenges in practical deployments. Conventional defense mechanisms struggle to address the evolving landscape of multifaceted attack patterns. Through systematic analysis of existing defenses, we identify that their unsatisfactory performance primarily originates from an entangled feature space, where adversarial attacks can be performed easily. To this end, we present 3D-ANC, a novel approach that capitalizes on the Neural Collapse (NC) mechanism to orchestrate discriminative feature learning. In particular, NC depicts where last-layer features and classifier weights jointly evolve into a simplex equiangular tight frame (ETF) arrangement, establishing maximally separable class prototypes. However, leveraging this advantage in 3D recognition confronts two substantial challenges: (1) prevalent class imbalance in point cloud datasets, and (2) complex geometric similarities between object categories. To tackle these obstacles, our solution combines an ETF-aligned classification module with an adaptive training framework consisting of representation-balanced learning (RBL) and dynamic feature direction loss (FDL). 3D-ANC seamlessly empowers existing models to develop disentangled feature spaces despite the complexity in 3D data distribution. Comprehensive evaluations state that 3D-ANC significantly improves the robustness of models with various structures on two datasets. For instance, DGCNN's classification accuracy is elevated from 27.2% to 80.9% on ModelNet40 -- a 53.7% absolute gain that surpasses leading baselines by 34.0%.

Time series classification (TSC) is a cornerstone of modern web applications, powering tasks such as financial data analysis, network traffic monitoring, and user behavior analysis. In recent years, deep neural networks (DNNs) have greatly enhanced the performance of TSC models in these critical domains. However, DNNs are vulnerable to backdoor attacks, where attackers can covertly implant triggers into models to induce malicious outcomes. Existing backdoor attacks targeting DNN-based TSC models remain elementary. In particular, early methods borrow trigger designs from computer vision, which are ineffective for time series data. More recent approaches utilize generative models for trigger generation, but at the cost of significant computational complexity.

Deep neural networks have demonstrated remarkable performance in point cloud classification. However, pre-vious works show they are vulnerable to adversarial per-turbations that can manipulate their predictions. Given the distinctive modality of point clouds, various attack strategies have emerged, posing challenges for existing defenses to achieve effective generalization. In this study, we for the first time introduce causal modeling to enhance the robustness of point cloud classification models. Our insight is from the observation that adversarial examples closely re-semble benign point clouds from the human perspective. In our causal modeling, we incorporate two critical variables, the structural information, (standing for the key feature leading to the classification) and the hidden confounders, (standing for the noise interfering with the classification). The resulting overall framework CausalPC consists of three sub-modules to identify the causal effect for robust classification. The framework is model-agnostic and adaptable for integration with various point cloud classifiers. Our approach significantly improves the adversarial robustness of three mainstream point cloud classification models on two benchmark datasets. For instance, the classification accuracy for DGCNN on ModelNet40 increases from 29.2% to 72.0% with CausalPC, whereas the best-performing base-line achieves only 42.4%.

With the increasing use of deep neural network (DNN) in time series classification (TSC), recent work reveals the threat of adversarial attack, where the adversary can construct adversarial examples to cause model mistakes. However, existing researches on the adversarial attack of TSC typically adopt an unrealistic white-box setting with model details transparent to the adversary. In this work, we study a more rigorous black-box setting with attack detection applied, which restricts gradient access and requires the adversarial example to be also stealthy. Theoretical analyses reveal that the key lies in: estimating black-box gradient with diversity and non-convexity of TSC models resolved, and restricting the l0 norm of the perturbation to construct adversarial samples. Towards this end, we propose a new framework named BlackTreeS, which solves the hard optimization issue for adversarial example construction with two simple yet effective modules. In particular, we propose a tree search strategy to find influential positions in a sequence, and independently estimate the black-box gradients for these positions. Extensive experiments on three real-world TSC datasets and five DNN based models validate the effectiveness of BlackTreeS, e.g., it improves the attack success rate from 19.3% to 27.3%, and decreases the detection success rate from 90.9% to 6.8% for LSTM on the UWave dataset.

Recently, deep neural networks have shown great success on 3D point cloud classification tasks, which simultaneously raises the concern of adversarial attacks that cause severe damage to real-world applications. Moreover, defending against adversarial examples in point cloud data is extremely difficult due to the emergence of various attack strategies. In this work, with the insight of the fact that the adversarial examples in this task still preserve the same semantic and structural information as the original input, we design a novel defense framework for improving the robustness of existing classification models, which consists of two main modules: the attention-based pooling and the dynamic contrastive learning. In addition, we also develop an algorithm to theoretically certify the robustness of the proposed framework. Extensive empirical results on two datasets and three classification models show the robustness of our approach against various attacks, e.g., the averaged attack success rate of PointNet decreases from 70.2% to 2.7% on the ModelNet40 dataset under 9 common attacks.

As a fundamental task in modern data mining, time series classification is powering mission-critical tasks including stock price prediction and network traffic analysis. Due to the non-linear structure of deep neural networks (DNN), deep learning has established as a promising solution to time series classification. However, the excessive learning capacity of DNNs may make them prone to threats of backdoor attacks, where an attacker embeds hidden functionalities (i.e., backdoor) to DNNs and activates the backdoor by specially-designed inputs (i.e., triggers). Despite extensive studies concerning backdoor attacks on image and text domains, there is little known about the vulnerability of DNN based time series classifiers against backdoor attacks. Due to the unique characteristics of time series data, most existing backdoor attack techniques fail to threaten time series classifiers. In this paper, through analyzing the key factors which influence the effectiveness of a backdoor, we systematize a list of practical principles for designing triggers on time series data. In this light, we propose a novel framework called TimeTrojan, which aims to learn to form the trigger pattern through a constrained multi-objective optimization. To solve the hereafter challenging optimization issue, we further design an iterative learning algorithm. Remarkably, the proposed framework is agnostic to a wide range of DNN classifiers. Extensive empirical results on 6 representative DNN classifiers and 6 real-world datasets validate the effectiveness of the proposed attack framework. In most cases, TimeTrojan successfully injects backdoors with 100% attack success rate without affecting the model accuracy on clean samples, which implies the complete control of the behavior of the DNN classifiers by the adversary.

Time series prediction has great practical value in a wide range of real-world scenarios such as stock market and retail. Existing methods typically face model aging issue caused by the concept drift: the model performance degrades along time. Undoubtedly, the model aging issue can cause serious damage in practical usage, e.g. wrong predictions in stock price may cause catastrophic losses in the financial domain. Therefore, it is essential to address the model aging issue so as to promise the predictor's performance in the future. In this paper, we propose a novel solution to address the issue. First, we uncover the theoretical connection between the complex concept drift in time series data and the gradients of deep neural networks. Based on this, we propose a novel framework called learning to learn the future. Specifically, we develop a learning method to model the concept drift during the inference stage, which can help the model generalize well in the future. Furthermore, to mitigate the impact of noises and randomness of time series data, we propose to enhance the framework by leveraging similar series in concept drift modeling. To the best of our knowledge, our approach is the first general solution to model aging issue in time series prediction. We conduct extensive experiments on three real-world datasets, which validate the effectiveness of our framework. For instance, it achieves a relative improvement of 33% in stock price prediction over the state-of-the-art methods.