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Symmetry
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Applications Based on Symmetry and Asymmetry in Deep Learning and...
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Applications Based on Symmetry and Asymmetry in Deep Learning and Artificial Intelligence Methods

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2058

Special Issue Editors

Dr. Changshuo Wang

E-Mail Website
Guest Editor
Cyber Security Research Centre, Nanyang Technological University, Singapore, Singapore
Interests: scene understanding and generation; multimodal representation; human-centered visual understanding
Special Issues, Collections and Topics in MDPI journals
Dr. Zhijian Hu

E-Mail Website
Guest Editor
LAAS-CNRS, University of Toulouse, CNRS, 31400 Toulouse, France
Interests: cyber–physical systems, electrical power generation, control engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Meiqing Wu

E-Mail
Guest Editor
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Interests: stereo vision; motion analysis; object detection; tracking for urban traffic scene understanding

Special Issue Information

Dear Colleagues,

This Special Issue explores the fundamental role of symmetry and asymmetry principles in deep learning and artificial intelligence, with a special focus on 3D point cloud processing and computer vision applications. We investigate how symmetrical and asymmetrical patterns influence neural network architectures and learning algorithms, particularly in processing unordered point clouds and visual data. Key areas include symmetry-aware point cloud analysis, geometric deep learning, symmetry detection in 3D shapes, and invariant feature learning in computer vision tasks. This collection bridges theoretical symmetry concepts with practical implementations, advancing both algorithmic designs and real-world applications.

Dr. Changshuo Wang
Dr. Zhijian Hu
Dr. Meiqing Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • point cloud symmetry
  • neural network architecture
  • symmetry-aware feature learning
  • 3D shape analysis
  • computer vision symmetry
  • deep learning invariance
  • geometric deep learning
  • symmetry detection
  • vision transformers
  • symmetrical pattern recognition

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Published Papers (4 papers)

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Research

33 pages, 2533 KiB  
Article
VBTCKN: A Time Series Forecasting Model Based on Variational Mode Decomposition with Two-Channel Cross-Attention Network
by Zhiguo Xiao, Changgen Li, Huihui Hao, Siwen Liang, Qi Shen and Dongni Li
Symmetry 2025, 17(7), 1063; https://doi.org/10.3390/sym17071063 - 4 Jul 2025
Viewed by 341
Abstract
Time series forecasting serves a critical function in domains such as energy, meteorology, and power systems by leveraging historical data to predict future trends. However, existing methods often prioritize long-term dependencies while neglecting the integration of local features and global patterns, resulting in [...] Read more.
Time series forecasting serves a critical function in domains such as energy, meteorology, and power systems by leveraging historical data to predict future trends. However, existing methods often prioritize long-term dependencies while neglecting the integration of local features and global patterns, resulting in limited accuracy for short-term predictions of non-stationary multivariate sequences. To address these challenges, this paper proposes a time series forecasting model named VBTCKN based on variational mode decomposition and a dual-channel cross-attention network. First, the model employs variational mode decomposition (VMD) to decompose the time series into multiple frequency-complementary modal components, thereby reducing sequence volatility. Subsequently, the BiLSTM channel extracts temporal dependencies between sequences, while the transformer channel captures dynamic correlations between local features and global patterns. The cross-attention mechanism dynamically fuses features from both channels, enhancing complementary information integration. Finally, prediction results are generated through Kolmogorov–Arnold networks (KAN). Experiments conducted on four public datasets demonstrated that VBTCKN outperformed other state-of-the-art methods in both accuracy and robustness. Compared with BiLSTM, VBTCKN reduced RMSE by 63.32%, 68.31%, 57.98%, and 90.76%, respectively. Full article
(This article belongs to the Special Issue Applications Based on Symmetry and Asymmetry in Deep Learning and Artificial Intelligence Methods)
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24 pages, 2149 KiB  
Article
STA-3D: Combining Spatiotemporal Attention and 3D Convolutional Networks for Robust Deepfake Detection
by Jingbo Wang, Jun Lei, Shuohao Li and Jun Zhang
Symmetry 2025, 17(7), 1037; https://doi.org/10.3390/sym17071037 - 1 Jul 2025
Viewed by 456
Abstract
Recent advancements in deep learning have driven the rapid proliferation of deepfake generation techniques, raising substantial concerns over digital security and trustworthiness. Most current detection methods primarily focus on spatial or frequency domain features but show limited effectiveness when dealing with compressed videos [...] Read more.
Recent advancements in deep learning have driven the rapid proliferation of deepfake generation techniques, raising substantial concerns over digital security and trustworthiness. Most current detection methods primarily focus on spatial or frequency domain features but show limited effectiveness when dealing with compressed videos and cross-dataset scenarios. Observing that mainstream generation methods use frame-by-frame synthesis without adequate temporal consistency constraints, we introduce the Spatiotemporal Attention 3D Network (STA-3D), a novel framework that combines a lightweight spatiotemporal attention module with a 3D convolutional architecture to improve detection robustness. The proposed attention module adopts a symmetric multi-branch architecture, where each branch follows a nearly identical processing pipeline to separately model temporal-channel, temporal-spatial, and intra-spatial correlations. Our framework additionally implements Spatial Pyramid Pooling (SPP) layers along the temporal axis, enabling adaptive modeling regardless of input video length. Furthermore, we mitigate the inherent asymmetry in the quantity of authentic and forged samples by replacing standard cross entropy with focal loss for training. This integration facilitates the simultaneous exploitation of inter-frame temporal discontinuities and intra-frame spatial artifacts, achieving competitive performance across various benchmark datasets under different compression conditions: for the intra-dataset setting on FF++, it improves the average accuracy by 1.09 percentage points compared to existing SOTA, with a more significant gain of 1.63 percentage points under the most challenging C40 compression level (particularly for NeuralTextures, achieving an improvement of 4.05 percentage points); while for the intra-dataset setting, AUC is enhanced by 0.24 percentage points on the DFDC-P dataset. Full article
(This article belongs to the Special Issue Applications Based on Symmetry and Asymmetry in Deep Learning and Artificial Intelligence Methods)
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20 pages, 1336 KiB  
Article
Complex Question Decomposition Based on Causal Reinforcement Learning
by Dezhi Li, Yunjun Lu, Jianping Wu, Wenlu Zhou and Guangjun Zeng
Symmetry 2025, 17(7), 1022; https://doi.org/10.3390/sym17071022 - 29 Jun 2025
Viewed by 367
Abstract
Complex question decomposition is an important research topic in the field of natural language processing (NLP). It refers to the decomposition of a compound question containing multiple ontologies and classes into a simple question containing only a single attribute or entity. Most previous [...] Read more.
Complex question decomposition is an important research topic in the field of natural language processing (NLP). It refers to the decomposition of a compound question containing multiple ontologies and classes into a simple question containing only a single attribute or entity. Most previous studies focus on how to generate simple questions using a single attribute or entity but pay little attention to the generation order of simple questions, which may lead to an inaccurate decomposition or longer execution time. In this study, we propose a new method based on causal reinforcement learning, which combines the advantages of the current optimal performance reinforcement learning method and the causal inference method. Compared with previous methods, causal reinforcement learning can find the generation order of sub-questions more accurately, so as to better decompose complex questions. In particular, the prior knowledge is extracted using the counterfactual method in causal reasoning and is integrated into the policy network of the reinforcement learning model, and the reward rules of reinforcement learning are designed from the perspective of symmetry (positive reward and negative punishment), thus the intelligent body is guided to choose the sub-question with a greater benefit and less risk of decomposing. We compare the proposed method with the baseline method on three datasets. The experimental results show that the performance of our method is improved by 5–10% compared with the baseline method on Hits@n (n = 1, 3, 10), which proves the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Applications Based on Symmetry and Asymmetry in Deep Learning and Artificial Intelligence Methods)
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31 pages, 9659 KiB  
Article
Full-Element Analysis of Side-Channel Leakage Dataset on Symmetric Cryptographic Advanced Encryption Standard
by Weifeng Liu, Wenchang Li, Xiaodong Cao, Yihao Fu, Juping Wu, Jian Liu, Aidong Chen, Yanlong Zhang, Shuo Wang and Jing Zhou
Symmetry 2025, 17(5), 769; https://doi.org/10.3390/sym17050769 - 15 May 2025
Viewed by 551
Abstract
The application of deep learning in side-channel analysis faces critical challenges arising from dispersed public datasets—i.e., datasets collected from heterogeneous sources and platforms with varying formats, labeling schemes, and sampling settings—and insufficient sample distribution uniformity, characterized by imbalanced class distributions and long-tailed label [...] Read more.
The application of deep learning in side-channel analysis faces critical challenges arising from dispersed public datasets—i.e., datasets collected from heterogeneous sources and platforms with varying formats, labeling schemes, and sampling settings—and insufficient sample distribution uniformity, characterized by imbalanced class distributions and long-tailed label samples. This paper presents a systematic analysis of symmetric cryptographic AES side-channel leakage datasets, examining how these issues impact the performance of deep learning-based side-channel analysis (DL-SCA) models. We analyze over 10 widely used datasets, including DPA Contest and ASCAD, and highlight key inconsistencies via visualization, statistical metrics, and model performance evaluations. For instance, the DPA_v4 dataset exhibits extreme label imbalance with a long-tailed distribution, while the ASCAD datasets demonstrate missing leakage features. Experiments conducted using CNN and Transformer models show that such imbalances lead to high accuracy for a few labels (e.g., label 14 in DPA_v4) but also extremely poor accuracy (<0.5%) for others, severely degrading generalization. We propose targeted improvements through enhanced data collection protocols, training strategies, and feature alignment techniques. Our findings emphasize that constructing balanced datasets covering the full key space is vital to achieving robust and generalizable DL-SCA performance. This work contributes both empirical insights and methodological guidance for standardizing the design of side-channel datasets. Full article
(This article belongs to the Special Issue Applications Based on Symmetry and Asymmetry in Deep Learning and Artificial Intelligence Methods)
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