The rapid deployment of advanced metering infrastructure facilitates the development of data-driven attack detection methods in smart grids, which typically rely on large amounts of labeled data for training. However, when new types of attacks or faults emerge, security analysts may only intercept limited malicious samples. The scarcity of samples makes it difficult for data-driven methods to learn effective decision boundaries, leading to degraded detection performance. In this work, we bridge the gap by learning class prototype representations from limited samples and learning to match unlabeled samples with corresponding prototypes. Specifically, we propose a meta-learning-based framework termed Learning to Match Prototype (L2MP), which consists of a prototypical network (ProtoNet) that learns prototype representations by aggregating features from labeled samples, and a matching network that assesses the matching degree between unlabeled samples and prototypes for classification. Through episodic training designed to simulate the few-shot setting, L2MP learns to adapt to novel attack and fault types with only a few samples per class. Moreover, we utilize a bilevel optimization strategy to ensure efficient training of both networks. Extensive case studies on smart grid datasets demonstrate that L2MP achieves robust performance under harsh learning conditions and has practical utility in real-world scenarios.

Despite the success of data-driven methods in detecting false data injection (FDI) attacks, the remarkable progress is inseparable from massive labeled and class-balanced measurements. However, the collected measurement datasets in smart grids typically exhibit skewed class distributions and are partially labeled due to the expensive labeling costs. Learning from such non-ideal datasets undoubtedly results in the degenerated detection performance of the data-driven methods. To cope with this issue, we propose an optimal transport (OT)-based framework named DeSSW to promote the utilization of plentiful unlabeled measurements through the self-training technique, which improves the ability to identify FDI attacks by producing distinguishable representations for normal and attacked measurements in the feature space. Specifically, DeSSW consists of a novel re-weighting algorithm and a debiased self-training strategy. The re-weighting algorithm ensures high-confidence unlabeled measurements dominate the self-training procedure, and the debiased self-training strategy mitigates bias accumulation in the iterative self-training procedure. Extensive experiments demonstrate that DeSSW achieves superior detection performance when facing the combinatorial challenge of partially labeled and class-imbalanced measurements, even if the measurements are noisy.

Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely on high-quality manual annotations and assume that modalities among multi-modal data provide semantically relevant information. Unfortunately, the widely used video datasets are usually coarse-annotated or collected from the Internet. Thus, it inevitably contains a portion of noisy labels and noisy correspondence. To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence. Specifically, our method consists of two phases that aim to rectify noise by the inherent correlation between modalities. First, a noise-tolerant contrastive training phase is performed to make the model immune to the possible noisy-labeled data. Despite the benefits brought by contrastive training, it would overfit the noisy correspondence and thus provide false supervision. To alleviate the influence of noisy correspondence, we propose a cross-modal noise estimation component to adjust the consistency between different modalities. As the noisy correspondence existed at the instance level, we further propose a category-level contrastive loss to reduce its interference. Second, in the hybrid-supervised training phase, we calculate the distance metric among features to obtain corrected labels, which are used as complementary supervision to guide the training. Furthermore, due to the lack of suitable datasets, we establish a benchmark of real-world noisy correspondence in audio-visual data by relabeling the Kinetics dataset. Extensive experiments on a wide range of noisy levels demonstrate that our method significantly improves the robustness of the action recognition model and surpasses the baselines by a clear margin.

Despite the success of multimodal learning in crossmodal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting such ideal data is expensive and time-consuming. In practice, most widely used datasets are harvested from the Internet and inevitably contain mismatched pairs. Training on such noisy correspondence datasets causes performance degradation because the cross-modal retrieval methods can wrongly enforce the mismatched data to be similar. To tackle this problem, we propose a Meta Similarity Correction Network (MSCN) to provide reliable similarity scores. We view a binary classification task as the meta-process that encourages the MSCN to learn discrimination from positive and negative meta-data. To further alleviate the influence of noise, we design an effective data purification strategy using meta-data as prior knowledge to remove the noisy samples. Extensive experiments are conducted to demonstrate the strengths of our method in both synthetic and real-world noises, including Flickr30K, MS-COCO, and Conceptual Captions. Our code is publicly available.11https://github.com/hhc1997/MSCN