Federated learning (FL) is vulnerable to data poisoning attacks when an adversary attempts to upload poison gradients with the intent to corrupt the global model of FL. Various approaches have been proposed to counter these risks. However, it becomes challenging when one tries to preserve the privacy of FL participants and ensure robustness against data poisoning attacks. In this paper, we propose DPFLA, a novel scheme that can detect poisoning attacks without revealing the actual gradients of participants. DPFLA is a lossless aggregation scheme delicately designed for adopting masks to protect private data while extracting poisoned data features. Specifically, we first apply removable masks to the gradients outputted by each participant. Second, we aggregate the masked data and decompose them using Singular Value Decomposition (SVD) to extract specific features as well as achieve dimensionality reduction. Third, we leverage a clustering paradigm to detect poison gradients from the low dimension and eliminate them in the following training rounds. We conducted extensive experiments to demonstrate that DPFLA can detect poison gradients effectively. Additionally, the comparisons of case studies demonstrate that DPFLA outperforms the state-of-the-art methods.

Short-term traffic flow forecasting is an essential part of intelligent transportation systems. However, it is challenging to model traffic flow accurately due to its rapid changes over time. The Kolmogorov–Arnold Network (KAN) has shown parameter efficiency with lower memory and computational overhead via spline-parametrized functions to handle high-dimensional temporal data. In this paper, we propose to unlock the potential of the Kolmogorov–Arnold network for traffic flow forecasting by optimizing its parameters with a heuristic algorithm. The gravitational search algorithm learns to understand optimized KANs for different traffic scenarios. We conduct extensive experiments on four real-world benchmark datasets from Amsterdam, the Netherlands. The RMSE of GSA-KAN is reduced by $3.95\%$, $6.96\%$, $2.71\%$, and $2.29\%$, and the MAPE of GSA-KAN is reduced by $6.66\%$, $5.88\%$, $6.41\%$, and $4.87\%$ on the A1, A2, A4, and A8 datasets, respectively. The experimental results demonstrate that GSA-KAN performs advanced parametric and nonparametric models.

Digital watermarking is an effective solution to the problem of copyright protection, thus maintaining the security of digital products in the network. An improved scheme to increase the robustness of embedded information on the basis of discrete cosine transform (DCT) domain is proposed in this study. The embedding process consisted of two main procedures. Firstly, the embedding intensity with support vector machines (SVMs) was adaptively strengthened by training 1600 image blocks which are of different texture and luminance. Secondly, the embedding position with the optimized genetic algorithm (GA) was selected. To optimize GA, the best individual in the first place of each generation directly went into the next generation, and the best individual in the second position participated in the crossover and the mutation process. The transparency reaches 40.5 when GA’s generation number is 200. A case study was conducted on a 256 × 256 standard Lena image with the proposed method. After various attacks (such as cropping, JPEG compression, Gaussian low-pass filtering (3,0.5), histogram equalization, and contrast increasing (0.5,0.6)) on the watermarked image, the extracted watermark was compared with the original one. Results demonstrate that the watermark can be effectively recovered after these attacks. Even though the algorithm is weak against rotation attacks, it provides high quality in imperceptibility and robustness and hence it is a successful candidate for implementing novel image watermarking scheme meeting real timelines.

Accurate spatiotemporal traffic modeling and prediction are essential for intelligent transportation systems (ITS). Conventional models struggle with complex spatiotemporal variation and dynamic traffic patterns. The spatiotemporal heterogeneity of different road networks is challenging, which limits the models to accurately capture traffic flow patterns. In this paper, we propose a quantum-inspired dynamic spatiotemporal matching transformer (DSTMT) to achieve more robust and accurate traffic flow forecasting. We first design a temporal pattern embedding to capture temporal heterogeneity to improve the accuracy of traffic flow forecasting. Then, we design a memory-augmented pattern matching to extract the representative traffic pattern for each traffic node. We also develop a quantum-inspired adaptive transfer mechanism to mitigate the error caused by the discrepancy between historical traffic flow and forecasting. After that, we introduce an iTransformer to capture the spatial heterogeneity for each traffic node to improve prediction performance. Extensive experiments are conducted on five real-world datasets compared with 21 state-of-the-art methods and 7 baselines. The experimental results demonstrate the superior performance of DSTMT for traffic flow forecasting.

Combining Graph neural networks (GNNs) with contrastive learning for anomaly detection has drawn rising attention recently. Existing graph contrastive anomaly detection (GCAD) methods have primarily focused on improving detection capability through graph augmentation and multi-scale contrast modules. However, the underlying mechanisms of how these modules work have not been fully explored. We dive into the multi-scale and graph augmentation mechanism and observed that multi-scale contrast modules do not enhance the expression, while the multi-GNN modules are the hidden contributors. Previous studies have tended to attribute the benefits brought by multi-GNN to the multi-scale modules. In the paper, we delve into the misconception and propose Multi-GNN and Augmented Graph contrastive framework MAG, which unified the existing GCAD methods in the contrastive self-supervised perspective. We extracted two variants from the MAG framework, L-MAG and M-MAG. The L-MAG is the lightweight instance of the MAG, which outperform the state-of-the-art on Cora and Pubmed with the low computational cost. The variant M-MAG equipped with multi-GNN modules further improve the detection performance. Our study sheds light on the drawback of the existing GCAD methods and demonstrates the potential of multi-GNN and graph augmentation modules. Our code is available at https://anonymous.4open.science/r/MAG-Framework-74D0.

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.