RoboKA: KAN Informed Multimodal Learning for RoboCall Surveillance System
Abstract
Wide exploration on robocall surveillance research is hindered due to limited access to public datasets, due to privacy concerns. In this work, we first curate Robo-SAr, a synthetic robocall dataset designed for robocall surveillance research. Robo-SAr comprises of ~200 unwanted and ~1200 legitimate synthetic robocall samples across three realistic adversarial axes: psycholinguistics-manipulated transcripts, emotion-eliciting speech, and cloned voices. We further propose RoboKA, a Kolmogorov-Arnold Network (KAN)-based multimodal fusion framework designed to model structured nonlinear interactions between acoustic and linguistic cues that characterize diverse adversarial robocall strategies. RoboKA first leverages cross-modal contrastive learning to align latent modality representations and feeds the resulting embeddings to a KAN-projection head for final classification. We benchmark RoboKA against strong unimodal and multimodal baselines in both in-domain and out-of-domain setups, finding RoboKA to surpass all baselines in terms of recall and F1-score.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of Robo-SAr, a novel adversarial dataset for robocall surveillance, and the development of RoboKA, a KAN-informed multimodal framework that significantly improves the detection of unwanted calls. This work addresses critical gaps in the field by providing a comprehensive approach to modeling the complex interactions between audio and linguistic cues, thereby advancing the state of the art in robocall detection.
Methodology
The methodology presented in this paper is robust and innovative, leveraging a novel dataset (Robo-SAr) that addresses the limitations of existing datasets in robocall research. The use of Kolmogorov-Arnold Networks (KAN) for multimodal fusion is a significant advancement, as it allows for the modeling of complex nonlinear interactions between audio and text modalities. The cross-modal contrastive learning approach enhances the alignment of representations, which is crucial for effective robocall detection. The authors also provide a clear explanation of their methods and the rationale behind their choices, making the methodology both sound and well-justified.
Results & evaluation
The experimental evaluation is comprehensive, benchmarking RoboKA against various unimodal and multimodal baselines under different conditions, including in-domain and out-of-domain setups. The results demonstrate a clear performance advantage for RoboKA, particularly in challenging scenarios, which underscores the effectiveness of the proposed approach. The use of human validation for the dataset adds credibility to the findings, although the paper could benefit from more detailed statistical analysis of the results.
Reproducibility
The paper commits to releasing the dataset and code upon review, which is a positive step towards ensuring reproducibility. However, the lack of explicit URLs for accessing the dataset and code is a drawback. The methodology is described in sufficient detail to allow for replication, but the absence of a demo or project URL limits immediate accessibility for other researchers.
Limitations
The paper acknowledges several limitations, including the focus on English language robocalls, which restricts the applicability of the findings to multilingual contexts. Additionally, the reliance on synthetic data raises questions about the generalizability of the results to real-world scenarios. The authors also note that the dataset may not fully capture the complexities of real-world robocalls, which could impact the robustness of the model in practical applications.
Broader impact
The implications of this research are significant, particularly in the context of increasing robocall threats. By providing a robust framework for detecting deceptive robocalls, this work has the potential to enhance consumer protection and inform regulatory efforts. The methodology could also be adapted for other domains where multimodal deception detection is relevant, such as phishing or online scams. The main contribution of this paper is the introduction of Robo-SAr, a novel adversarial dataset for robocall surveillance, and the development of RoboKA, a KAN-informed multimodal framework that significantly improves the detection of unwanted calls. This work addresses critical gaps in the field by providing a comprehensive approach to modeling the complex interactions between audio and linguistic cues, thereby advancing the state of the art in robocall detection.
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