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題名 基於異質靜態與動態分析模態的組合式潛在空間學習
Learning Compositional Latent Space from Heterogeneous Static-Dynamic Analysis Modalities
作者 姚昀誠
Yao, Yun-Cheng
貢獻者 蕭舜文
Hsiao, Shun-Wen
姚昀誠
Yao, Yun-Cheng
關鍵詞 多模態融合
潛在空間解耦
惡意家族分類
模型可解釋性
Multimodal Fusion
Latent Space Disentanglement
Malware Family Classification
Model Interpretability
日期 2025
上傳時間 1-Sep-2025 15:06:14 (UTC+8)
摘要 多模態惡意程式分析為一種新興且具發展潛力之方法，藉由整合不同資料來源（如組合語言程式碼與API呼叫序列）以提升威脅偵測效能。然而，現有方法存在重要限制，包含長序列資訊擷取不足、共享語意與模態特定資訊相互混雜，以及表示學習缺乏可解釋性等問題。為解決上述挑戰，本研究提出自監督多模態協調與解耦網路（Self-Supervised Multimodal Coordination & Disentanglement Network, SMCDN），該網路能明確分離核心惡意程式語意與模態特定之私有資訊。本框架採用雙向GRU編碼器以保留序列脈絡資訊、結合對比學習之雙模態變分自編碼器以擷取協調之共享表示，並透過獨立性約束進行私有空間解耦。此外，本研究引入資訊貢獻比率指標，用以量化各模態對語意理解之重要性，進而提升模型可解釋性。實驗採用包含10,344個樣本、涵蓋29個惡意程式家族之真實資料集進行驗證。結果顯示，本方法達成74.9% 之分類準確率，較傳統融合方法提升約8%。資訊貢獻分析結果顯示，API呼叫序列對共享語意之貢獻顯著高於組合語言程式碼（6.91 vs 1.00），為惡意程式分析提供重要見解。
Multimodal malware analysis has emerged as a promising approach to improve threat detection by integrating diverse data sources such as assembly code and API call sequences. However, existing approaches face critical limitations including inadequate information extraction from long sequences, mixture of shared semantics with modality-specific information, and lack of interpretability in representation learning. To address these challenges, we propose a Self-Supervised Multimodal Coordination & Disentanglement Network (SMCDN) that explicitly separates core malware semantics from modality-specific private information. Our framework employs bidirectional GRU-based encoders to preserve sequential context, dual modality VAEs with contrastive learning to extract coordinated shared representations, and independence constraints to disentangle private spaces. We introduce an information contribution ratio metric that quantifies each modality’s importance for semantic understanding, providing model interpretability. Extensive experiments on the real-world malware dataset containing 10,344 samples across 29 families demonstrate that our approach achieves 74.9% classification accuracy, outperforming traditional fusion methods by approximately 8%. The information contribution analysis reveals that API call sequences contribute significantly more to shared semantics compared to assembly code (6.91 vs 1.00), providing valuable insights for malware analysis.
參考文獻 Alzahrani, S., Xiao, Y., Asiri, S., Alasmari, N., & Li, T. (2025). RansomFormer: A cross-modal transformer architecture for ransomware detection via the fusion of byte and API features. Electronics, 14(7). Arrowsmith, J., Susnjak, T., & Jang-Jaccard, J. (2025). Multimodal deep learning for Android malware classification. MDPI. AV-TEST Institute. (2024). Malware statistics & trends report. AV-TEST GmbH. https://www.av-test.org/en/statistics/malware/ Bensaoud, A., Kalita, J., & Bensaoud, M. (2024). A survey of malware detection using deep learning. Machine Learning with Applications, 16, 100546. https://doi.org/10.1016/j.mlwa.2024.100546 Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (Article 149). JMLR.org. Cuckoo Foundation. (2010). Cuckoo Sandbox [GitHub repository]. GitHub. https://github.com/cuckoosandbox de Oliveira, A. S., & Sassi, R. J. (2023). Chimera: an android malware detection method based on multimodal deep learning and hybrid analysis. Authorea Preprints. Authorea. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., & Steiner, A. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. Geng, X., Liu, H., Lee, L., Schuurams, D., Levine, S., & Abbeel, P. (2022). Multimodal masked autoencoders learn transferable representations. arXiv preprint arXiv:2205.14204. Gibert, D., Mateu, C., & Planes, J. (2020). HYDRA: A multimodal deep learning framework for malware classification. Computers & Security, 95, 101873. https://doi.org/10.1016/j.cose.2020.101873 Gretton, A., Bousquet, O., Smola, A., & Schölkopf, B. (2005). Measuring statistical dependence with Hilbert-Schmidt norms. In International Conference on Algorithmic Learning Theory (pp. 63-77). Springer. Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., & Yin, J. (2022). UniXcoder: Unified cross-modal pre-training for code representation. arXiv preprint arXiv:2203.03850. Hsu, W. N., & Glass, J. (2018). Disentangling by partitioning: A representation learning framework for multimodal sensory data. arXiv preprint arXiv:1805.11264. Huang, Y. T., Lin, C. Y., Guo, Y. R., Lo, K. C., Sun, Y. S., & Chen, M. C. (2021). Open source intelligence for malicious behavior discovery and interpretation. IEEE Transactions on Dependable and Secure Computing, 19(2), 776-789. Khattar, D., Goud, J. S., Gupta, M., & Varma, V. (2019). MVAE: Multimodal variational autoencoder for fake news detection. In The World Wide Web Conference (pp. 2915-2921). ACM. https://doi.org/10.1145/3308558.3313552 Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T. L., Bansal, M., & Liu, J. (2021). Less is more: CLIPBERT for video-and-language learning via sparse sampling. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7327-7337). IEEE Computer Society. https://doi.org/10.1109/CVPR46437.2021.00725 Li, S., & Tang, H. (2024). Multimodal alignment and fusion: A survey. arXiv preprint arXiv:2411.17040. Li, X., Liu, L., Liu, Y., & Liu, H. (2025). Detecting Android malware: A multimodal fusion method with fine-grained feature. Information Fusion, 114, 102662. https://doi.org/10.1016/j.inffus.2024.102662 Li, X., Liu, L., Liu, Y., Zhao, Y., Zhang, P., & Liu, H. (2025). Multimodal fusion for Android malware detection based on large pre-trained models. IEEE Transactions on Software Engineering. IEEE. Liang, P. P., Deng, Z., Ma, M. Q., Zou, J. Y., Morency, L. P., & Salakhutdinov, R. (2023). Factorized contrastive learning: Going beyond multi-view redundancy. Advances in Neural Information Processing Systems, 36, 32971-32998. Liang, P. P., Zadeh, A., & Morency, L. P. (2024). Foundations & trends in multimodal machine learning: Principles, challenges, and open questions. ACM Computing Surveys, 56(10), Article 264. https://doi.org/10.1145/3656580 Liang, V. W., Zhang, Y., Kwon, Y., Yeung, S., & Zou, J. Y. (2022). Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning. Advances in Neural Information Processing Systems, 35, 17612-17625. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692. Microsoft. (2025). How Microsoft names malware - Unified security operations. https://learn.microsoft.com/en-us/unified-secops-platform/malware-naming Mohammed, T. M., Nataraj, L., Chikkagoudar, S., Chandrasekaran, S., & Manjunath, B. S. (2021). Malware detection using frequency domain-based image visualization and deep learning. arXiv preprint arXiv:2101.10578. National Security Agency. (2019). Ghidra [GitHub repository]. GitHub. https://github.com/NationalSecurityAgency/ghidra Onwuzurike, L., Mariconti, E., Andriotis, P., Cristofaro, E. D., Ross, G., & Stringhini, G. (2019). MaMaDroid: Detecting Android malware by building Markov chains of behavioral models (extended version). ACM Transactions on Privacy and Security, 22(2), Article 14. https://doi.org/10.1145/3313391 Pawłowski, M., Wróblewska, A., & Sysko-Romańczuk, S. (2023). Effective techniques for multimodal data fusion: A comparative analysis. Sensors, 23(5), Article 2381. https://doi.org/10.3390/s23052381 Poklukar, P., Vasco, M., Yin, H., Melo, F. S., Paiva, A., & Kragic, D. (2022). Geometric multimodal contrastive representation learning. In International Conference on Machine Learning (pp. 17782-17800). PMLR. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (pp. 8748-8763). PMLR. Sun, C., Myers, A., Vondrick, C., Murphy, K., & Schmid, C. (2019). VideoBERT: A joint model for video and language representation learning. In 2019 IEEE/CVF International Conference on Computer Vision (pp. 7463-7472). IEEE Computer Society. https://doi.org/10.1109/ICCV.2019.00756 Tsai, Y. H. H., Liang, P. P., Zadeh, A., Morency, L. P., & Salakhutdinov, R. (2018). Learning factorized multimodal representations. arXiv preprint arXiv:1806.06176. Ucci, D., Aniello, L., & Baldoni, R. (2019). Survey of machine learning techniques for malware analysis. Computers & Security, 81, 123-147. https://doi.org/10.1016/j.cose.2018.11.001 VirusTotal. (2025). VirusTotal - Home. https://www.virustotal.com/gui/home/upload Wong, G. W., Huang, Y. T., Guo, Y. R., Sun, Y., & Chen, M. C. (2023). Attention-based API locating for malware techniques. IEEE Transactions on Information Forensics and Security, 19, 1199-1212. Wu, D., Zhao, Y., Tsai, Y. H. H., Yamada, M., & Salakhutdinov, R. (2018). "Dependency bottleneck" in auto-encoding architectures: An empirical study. arXiv preprint arXiv:1802.05408. Yoon, J., Kang, C., Kim, S., & Han, J. (2022). D-vlog: Multimodal vlog dataset for depression detection. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12226-12234. https://doi.org/10.1609/aaai.v36i11.21483 Zhao, F., Zhang, C., & Geng, B. (2024). Deep multimodal data fusion. ACM Computing Surveys, 56(9), Article 216. https://doi.org/10.1145/3649447
描述 碩士
國立政治大學
資訊管理學系
112356046
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112356046
資料類型 thesis
dc.contributor.advisor 蕭舜文zh_TW
dc.contributor.advisor Hsiao, Shun-Wenen_US
dc.contributor.author (Authors) 姚昀誠zh_TW
dc.contributor.author (Authors) Yao, Yun-Chengen_US
dc.creator (作者) 姚昀誠zh_TW
dc.creator (作者) Yao, Yun-Chengen_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Sep-2025 15:06:14 (UTC+8)-
dc.date.available 1-Sep-2025 15:06:14 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2025 15:06:14 (UTC+8)-
dc.identifier (Other Identifiers) G0112356046en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159100-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 112356046zh_TW
dc.description.abstract (摘要) 多模態惡意程式分析為一種新興且具發展潛力之方法，藉由整合不同資料來源（如組合語言程式碼與API呼叫序列）以提升威脅偵測效能。然而，現有方法存在重要限制，包含長序列資訊擷取不足、共享語意與模態特定資訊相互混雜，以及表示學習缺乏可解釋性等問題。為解決上述挑戰，本研究提出自監督多模態協調與解耦網路（Self-Supervised Multimodal Coordination & Disentanglement Network, SMCDN），該網路能明確分離核心惡意程式語意與模態特定之私有資訊。本框架採用雙向GRU編碼器以保留序列脈絡資訊、結合對比學習之雙模態變分自編碼器以擷取協調之共享表示，並透過獨立性約束進行私有空間解耦。此外，本研究引入資訊貢獻比率指標，用以量化各模態對語意理解之重要性，進而提升模型可解釋性。實驗採用包含10,344個樣本、涵蓋29個惡意程式家族之真實資料集進行驗證。結果顯示，本方法達成74.9% 之分類準確率，較傳統融合方法提升約8%。資訊貢獻分析結果顯示，API呼叫序列對共享語意之貢獻顯著高於組合語言程式碼（6.91 vs 1.00），為惡意程式分析提供重要見解。zh_TW
dc.description.abstract (摘要) Multimodal malware analysis has emerged as a promising approach to improve threat detection by integrating diverse data sources such as assembly code and API call sequences. However, existing approaches face critical limitations including inadequate information extraction from long sequences, mixture of shared semantics with modality-specific information, and lack of interpretability in representation learning. To address these challenges, we propose a Self-Supervised Multimodal Coordination & Disentanglement Network (SMCDN) that explicitly separates core malware semantics from modality-specific private information. Our framework employs bidirectional GRU-based encoders to preserve sequential context, dual modality VAEs with contrastive learning to extract coordinated shared representations, and independence constraints to disentangle private spaces. We introduce an information contribution ratio metric that quantifies each modality’s importance for semantic understanding, providing model interpretability. Extensive experiments on the real-world malware dataset containing 10,344 samples across 29 families demonstrate that our approach achieves 74.9% classification accuracy, outperforming traditional fusion methods by approximately 8%. The information contribution analysis reveals that API call sequences contribute significantly more to shared semantics compared to assembly code (6.91 vs 1.00), providing valuable insights for malware analysis.en_US
dc.description.tableofcontents 1. INTRODUCTION 8 2. RELATED WORK 14 2.1 DEEP MULTIMODAL FUSION 14 2.1.1 Encoder-Decoder-based 14 2.1.2 Attention-based 15 2.1.3 Constraint-based 15 2.2 REPRESENTATION DISENTANGLEMENT 16 2.3 MULTIMODAL MALWARE ANALYSIS 17 3. PROPOSED METHOD 20 3.1 OVERVIEW 20 3.2 DATA PREPROCESSING 20 3.3 VECTOR EMBEDDING 23 3.4 SELF-SUPERVISED MULTIMODAL COORDINATION & DISENTANGLEMENT NETWORK 23 3.4.1 Bidirectional GRU-based Encoder 24 3.4.2 Coordination & Disentanglement Network 25 3.4.3 Decoder Network 27 4. EXPERIMENTS 29 4.1 DATASET 29 4.2 – RQ1: EFFECTIVENESS OF MULTIMODAL COORDINATION & DISENTANGLEMENT 30 4.3 – RQ2: COMPARISON WITH MULTIMODAL FUSION APPROACHES 35 4.4 – RQ3: INFORMATION-BASED INTERPRETATION 39 4.5 – RQ4: LONG SEQUENCE PROCESSING CAPABILITY 41 4.6 – RQ5: MISSING MODALITY ROBUSTNESS 41 5. CONCLUSION 43 REFERENCES 44zh_TW
dc.format.extent 3141040 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112356046en_US
dc.subject (關鍵詞) 多模態融合zh_TW
dc.subject (關鍵詞) 潛在空間解耦zh_TW
dc.subject (關鍵詞) 惡意家族分類zh_TW
dc.subject (關鍵詞) 模型可解釋性zh_TW
dc.subject (關鍵詞) Multimodal Fusionen_US
dc.subject (關鍵詞) Latent Space Disentanglementen_US
dc.subject (關鍵詞) Malware Family Classificationen_US
dc.subject (關鍵詞) Model Interpretabilityen_US
dc.title (題名) 基於異質靜態與動態分析模態的組合式潛在空間學習zh_TW
dc.title (題名) Learning Compositional Latent Space from Heterogeneous Static-Dynamic Analysis Modalitiesen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Alzahrani, S., Xiao, Y., Asiri, S., Alasmari, N., & Li, T. (2025). RansomFormer: A cross-modal transformer architecture for ransomware detection via the fusion of byte and API features. Electronics, 14(7). Arrowsmith, J., Susnjak, T., & Jang-Jaccard, J. (2025). Multimodal deep learning for Android malware classification. MDPI. AV-TEST Institute. (2024). Malware statistics & trends report. AV-TEST GmbH. https://www.av-test.org/en/statistics/malware/ Bensaoud, A., Kalita, J., & Bensaoud, M. (2024). A survey of malware detection using deep learning. Machine Learning with Applications, 16, 100546. https://doi.org/10.1016/j.mlwa.2024.100546 Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (Article 149). JMLR.org. Cuckoo Foundation. (2010). Cuckoo Sandbox [GitHub repository]. GitHub. https://github.com/cuckoosandbox de Oliveira, A. S., & Sassi, R. J. (2023). Chimera: an android malware detection method based on multimodal deep learning and hybrid analysis. Authorea Preprints. Authorea. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., & Steiner, A. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. Geng, X., Liu, H., Lee, L., Schuurams, D., Levine, S., & Abbeel, P. (2022). Multimodal masked autoencoders learn transferable representations. arXiv preprint arXiv:2205.14204. Gibert, D., Mateu, C., & Planes, J. (2020). HYDRA: A multimodal deep learning framework for malware classification. Computers & Security, 95, 101873. https://doi.org/10.1016/j.cose.2020.101873 Gretton, A., Bousquet, O., Smola, A., & Schölkopf, B. (2005). Measuring statistical dependence with Hilbert-Schmidt norms. In International Conference on Algorithmic Learning Theory (pp. 63-77). Springer. Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., & Yin, J. (2022). UniXcoder: Unified cross-modal pre-training for code representation. arXiv preprint arXiv:2203.03850. Hsu, W. N., & Glass, J. (2018). Disentangling by partitioning: A representation learning framework for multimodal sensory data. arXiv preprint arXiv:1805.11264. Huang, Y. T., Lin, C. Y., Guo, Y. R., Lo, K. C., Sun, Y. S., & Chen, M. C. (2021). Open source intelligence for malicious behavior discovery and interpretation. IEEE Transactions on Dependable and Secure Computing, 19(2), 776-789. Khattar, D., Goud, J. S., Gupta, M., & Varma, V. (2019). MVAE: Multimodal variational autoencoder for fake news detection. In The World Wide Web Conference (pp. 2915-2921). ACM. https://doi.org/10.1145/3308558.3313552 Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T. L., Bansal, M., & Liu, J. (2021). Less is more: CLIPBERT for video-and-language learning via sparse sampling. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7327-7337). IEEE Computer Society. https://doi.org/10.1109/CVPR46437.2021.00725 Li, S., & Tang, H. (2024). Multimodal alignment and fusion: A survey. arXiv preprint arXiv:2411.17040. Li, X., Liu, L., Liu, Y., & Liu, H. (2025). Detecting Android malware: A multimodal fusion method with fine-grained feature. Information Fusion, 114, 102662. https://doi.org/10.1016/j.inffus.2024.102662 Li, X., Liu, L., Liu, Y., Zhao, Y., Zhang, P., & Liu, H. (2025). Multimodal fusion for Android malware detection based on large pre-trained models. IEEE Transactions on Software Engineering. IEEE. Liang, P. P., Deng, Z., Ma, M. Q., Zou, J. Y., Morency, L. P., & Salakhutdinov, R. (2023). Factorized contrastive learning: Going beyond multi-view redundancy. Advances in Neural Information Processing Systems, 36, 32971-32998. Liang, P. P., Zadeh, A., & Morency, L. P. (2024). Foundations & trends in multimodal machine learning: Principles, challenges, and open questions. ACM Computing Surveys, 56(10), Article 264. https://doi.org/10.1145/3656580 Liang, V. W., Zhang, Y., Kwon, Y., Yeung, S., & Zou, J. Y. (2022). Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning. Advances in Neural Information Processing Systems, 35, 17612-17625. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692. Microsoft. (2025). How Microsoft names malware - Unified security operations. https://learn.microsoft.com/en-us/unified-secops-platform/malware-naming Mohammed, T. M., Nataraj, L., Chikkagoudar, S., Chandrasekaran, S., & Manjunath, B. S. (2021). Malware detection using frequency domain-based image visualization and deep learning. arXiv preprint arXiv:2101.10578. National Security Agency. (2019). Ghidra [GitHub repository]. GitHub. https://github.com/NationalSecurityAgency/ghidra Onwuzurike, L., Mariconti, E., Andriotis, P., Cristofaro, E. D., Ross, G., & Stringhini, G. (2019). MaMaDroid: Detecting Android malware by building Markov chains of behavioral models (extended version). ACM Transactions on Privacy and Security, 22(2), Article 14. https://doi.org/10.1145/3313391 Pawłowski, M., Wróblewska, A., & Sysko-Romańczuk, S. (2023). Effective techniques for multimodal data fusion: A comparative analysis. Sensors, 23(5), Article 2381. https://doi.org/10.3390/s23052381 Poklukar, P., Vasco, M., Yin, H., Melo, F. S., Paiva, A., & Kragic, D. (2022). Geometric multimodal contrastive representation learning. In International Conference on Machine Learning (pp. 17782-17800). PMLR. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (pp. 8748-8763). PMLR. Sun, C., Myers, A., Vondrick, C., Murphy, K., & Schmid, C. (2019). VideoBERT: A joint model for video and language representation learning. In 2019 IEEE/CVF International Conference on Computer Vision (pp. 7463-7472). IEEE Computer Society. https://doi.org/10.1109/ICCV.2019.00756 Tsai, Y. H. H., Liang, P. P., Zadeh, A., Morency, L. P., & Salakhutdinov, R. (2018). Learning factorized multimodal representations. arXiv preprint arXiv:1806.06176. Ucci, D., Aniello, L., & Baldoni, R. (2019). Survey of machine learning techniques for malware analysis. Computers & Security, 81, 123-147. https://doi.org/10.1016/j.cose.2018.11.001 VirusTotal. (2025). VirusTotal - Home. https://www.virustotal.com/gui/home/upload Wong, G. W., Huang, Y. T., Guo, Y. R., Sun, Y., & Chen, M. C. (2023). Attention-based API locating for malware techniques. IEEE Transactions on Information Forensics and Security, 19, 1199-1212. Wu, D., Zhao, Y., Tsai, Y. H. H., Yamada, M., & Salakhutdinov, R. (2018). "Dependency bottleneck" in auto-encoding architectures: An empirical study. arXiv preprint arXiv:1802.05408. Yoon, J., Kang, C., Kim, S., & Han, J. (2022). D-vlog: Multimodal vlog dataset for depression detection. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12226-12234. https://doi.org/10.1609/aaai.v36i11.21483 Zhao, F., Zhang, C., & Geng, B. (2024). Deep multimodal data fusion. ACM Computing Surveys, 56(9), Article 216. https://doi.org/10.1145/3649447zh_TW