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Deepfake Detection Using Multimodal AI

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  • Lalit Kumar Joshi

    (System Administrator Mata Gujri College, Fatehgarh Sahib, Punjab, India)

  • Dr. Sangeeta Joshi

    (Department of Computer Science, Mata Gujri College, Fatehgarh Sahib, Punjab, India)

Abstract

Deepfakes, synthetic media generated using deep learning techniques, have grown rapidly in quality and prevalence, posing serious threats to digital trust, personal security, and political integrity. Traditional detection methods, primarily focused on single modalities such as image or audio analysis, have become increasingly ineffective against advanced generation techniques. This paper explores the use of multimodal AI systems, which integrate visual, audio, and textual cues, to enhance the accuracy and robustness of deepfake detection. We present a comprehensive overview of current multimodal detection techniques, compare their performance against unimodal approaches, and highlight challenges and future directions in building reliable, real-time detection systems [4].

Suggested Citation

  • Lalit Kumar Joshi & Dr. Sangeeta Joshi, 2025. "Deepfake Detection Using Multimodal AI," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 10(5), pages 355-357, May.
    • Handle: RePEc:bjf:journl:v:10:y:2025:i:5:p:355-357
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