Severe damage caused by transferable adversarial attacks has emerged as a prominent concern in recent years, especially in the smart grid. The security issue of the deep reinforcement learning (DRL)-based dynamic pricing system is directly related to the grid's reliability. Previous works have primarily focused on the attacks' transferability from the perspective of model architecture, whereas the concept of distribution bias offers a novel and relatively underexplored viewpoint. In this work, we propose transferable adversarial attacks with distribution (TAD) targeting the DRL model. The adversary emphasizes destroying the target model with the masqueraded malicious dataset while ensuring stealthiness. Concretely, the masqueraded dataset generated by the attacker is required to have a similar distribution to the original dataset, while perturb some of the critical samples to help the target model misdirect to the nonoptimal policy. To this end, we propose an innovative model named Masquerader, which leverages a variational auto-encoder and incorporates three elaborate loss functions to constrain the distribution and deviation of malicious samples. Extensive experiments in a DRL-based dynamic pricing system indicate that our attack strategy TAD could successfully perturb the target model's output. The aberrant flatness of retail prices and the grid system's reduction in daily profits further validate the attack's transferability and harmfulness.

Multiagent reinforcement learning (MARL) promises outstanding performance for multiintersection traffic signal control systems (TSCS), enabling intelligent administration of cities. However, the vulnerability of MARL algorithms to adversarial attacks has raised concerns about the security of TSCS. In this article, we explore the robustness of MARL-based TSCS against adversarial attacks, propose a black-box multiobject attack strategy, and assign an attack budget to ensure stealthiness. We design a dynamic threshold-based selection of critical states to minimize the cumulative reward with a limited number of attacks. In addition, we present a lightweight agnostic dynamic threshold-based defense mechanism by enhancing the worst-case performance of the policy. We formulate it as a min-max optimization problem, i.e., minimizing the quantity of training sample alterations while maximizing the cumulative discount reward of policy against the perturbed states. Extensive experiments on simulation of urban mobility (SUMO) demonstrate that the proposed attack policy can significantly reduce the performance of TSCS.

Water quality monitoring is crucial for ecological protection and water resource management. However, traditional monitoring methods suffer from limitations in temporal, spatial, and spectral resolution, which constrain the effective evaluation of urban rivers and multi-scale aquatic systems. To address challenges such as ecological heterogeneity, multi-scale complexity, and data noise, this paper proposes a deep learning framework, TL-Net, based on unmanned aerial vehicle (UAV) hyperspectral imagery, to estimate four water quality parameters: total nitrogen (TN), dissolved oxygen (DO), total suspended solids (TSS), and chlorophyll a (Chla); and to produce their spatial distribution maps. This framework integrates Transformer and long short-term memory (LSTM) networks, introduces a cross-temporal attention mechanism to enhance feature correlation, and incorporates an adaptive feature fusion module for dynamically weighted integration of local and global information. The experimental results demonstrate that TL-Net markedly outperforms conventional machine learning approaches, delivering consistently high predictive accuracy across all evaluated water quality parameters. Specifically, the model achieves an R² of 0.9938 for TN, a mean absolute error (MAE) of 0.0728 for DO, a root mean square error (RMSE) of 0.3881 for total TSS, and a mean absolute percentage error (MAPE) as low as 0.2568% for Chla. A spatial analysis reveals significant heterogeneity in water quality distribution across the study area, with natural water bodies exhibiting relatively uniform conditions, while the concentrations of TN and TSS are substantially elevated in aquaculture areas due to aquaculture activities. Overall, TL-Net significantly improves multi-parameter water quality prediction, captures fine-scale spatial variability, and offers a robust and scalable solution for inland aquatic ecosystem monitoring.

Hyperspectral anomaly detection (HAD) aims to classify each pixel in a hyperspectral image (HSI) as either background or anomaly without requiring labeled data. Traditional reconstruction based methods model the background using a predefined static background dictionary and low-rank representation coefficients. However, when anomalies are present, the use of a static dictionary can lead to inaccurate background representation, which is easily disturbed by anomalous points. Moreover, existing methods typically focus on the low-rank and smooth characteristics of the background during reconstruction, overlooking deeper features of the background representation. This motivates us to reconsider how the background should be represented. To address these issues, we propose an innovative HAD method that integrates background dictionary learning into the anomaly decomposition process. By using projection operators to optimize the background dictionary, we overcome the limitations of traditional methods that rely on static dictionaries. Additionally, we revisit the representation of the background and emphasize the importance of applying non-negative fullrank constraint to the representation coefficients under the new background dictionary. These improvements result in a more accurate background representation, thereby enhancing anomaly detection performance. Experimental results on several hyperspectral datasets demonstrate that the proposed algorithm excels in anomaly detection tasks, offering new insights and approaches for HAD.