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A defensive attention mechanism to detect deepfake content across multiple modalities

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Abstract

Recently, researchers have attracted much attention to the realistic nature of multi-modal deepfake content. They have employed plenty of handcrafted, learned features, and deep learning techniques to achieve promising performances for recognizing facial deepfakes. However, attackers continue to create deepfakes that outperform their earlier works by focusing on changes in many modalities, making deepfake identification under multiple modalities difficult. To exploit the merits of attention-based network architecture, we propose a novel cross-modal attention architecture on a bi-directional recurrent convolutional network to capture fake content in audio and video. For effective deepfake detection, the system records the spatial–temporal deformations of audio–video sequences and investigates the correlation in these modalities. We propose a self-attenuated VGG16 deep model for extracting visual features for facial fake recognition. Besides, the system incorporates a recurrent neural network with self-attention to extract false audio elements effectively. The cross-modal attention mechanism effectively learns the divergence between two modalities. Besides, we include multi-modal fake examples to create a well-balanced bespoke dataset to address the drawbacks of small and unbalanced training samples. We test the effectiveness of our proposed multi-modal deepfake detection strategy in comparison to state-of-the-art methods on a variety of existing datasets.

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Data availability

The data that support the fndings of this study are openly available in DFDC at https://paperswithcode.com/dataset/dfdc, MMDFD at https://dl.acm.org/doi/10.1145/3607947.3608013, FakeAVCeleb at https://paperswithcode.com/dataset/fakeavceleb.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SCMS School of Engineering and Technology, Ernakulam, India

    S. Asha

  2. Department of Computer Applications, Cochin University of Science and Technology, Cochin, India

    P. Vinod

  3. Department of Computer Science and Engineering, SCMS School of Engineering and Technology, Ernakulam, India

    Varun G. Menon

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  1. S. Asha
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  2. P. Vinod
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  3. Varun G. Menon
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Contributions

AS: Conceived and designed the analysis: contributed to the conceptualization of the research, including the formulation of the research questions and objectives. Collected the data: contributed to designing the research methodology, including data collection. Software development: developed and implemented the software used in the experiments. Performed the analysis: performed analysis using provided data and model. Writing, review and editing: responsible for the initial drafting of the manuscript, reviewed and edited the manuscript for clarity and coherence. PV: Conceived and designed the analysis: contributed to the conceptualization of the research, including the formulation of the research questions and objectives. Collected the data: involved in collecting and organizing the research data. Data analysis: Conducted data analysis and contributed to the interpretation of results. Experimental design: Contributed to the design of the experimental setup. Review and editing: reviewed and edited the manuscript for clarity and coherence. VGM: Data analysis: conducted data analysis and contributed to the interpretation of results. Experimental design: contributed to the design of the experimental setup. Writing—review and editing: contributed to manuscript review and editing. Supervision: provided overall supervision and guidance throughout the research project.

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Correspondence to S. Asha.

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The authors declare no competing interests.                                                                                                                                                                                    

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Communicated by I. Ide.

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Asha, S., Vinod, P. & Menon, V.G. A defensive attention mechanism to detect deepfake content across multiple modalities. Multimedia Systems 30, 56 (2024). https://doi.org/10.1007/s00530-023-01248-x

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