Conference Papers

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    Binarization in DeepFake Audio Detection: A Comparative Study and Performance Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2025) Gowhar, S.; Pandey, A.; Rudra, B.
    DeepFake audio, generated through advanced AI techniques, poses significant risks such as fraud, misinformation, and identity theft. As the quality of synthetic audio improves, detecting such fakes has become increasingly challenging. Traditional detection methods struggle to keep pace as AI-generated voices replicate speech patterns, tone, and pitch convincingly. While computationally intensive large-scale models can help detect DeepFakes generated by AI, their resource requirements make them impractical for deployment on mobile devices as well as on resource-constrained devices. This paper proposes a lightweight yet effective approach using binarized neural networks (BNNs) and further enhancements using additional dense layers and stacked modeling to overcome these challenges. We conduct a comprehensive performance analysis of the network and compare it with various machine learning and neural network methods to evaluate the tradeoff between detection accuracy and computational efficiency as an effect of binarization and precision loss in feature embeddings. © 2025 IEEE.
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    An Effective Approach for Deepfake Video Detection using Binarized Neural Network
    (Institute of Electrical and Electronics Engineers Inc., 2025) Praveen, K.; Pandey, A.; Rudra, B.
    The rise of DeepFake technologies, especially in audio and video, poses significant threats to information integrity, security, and privacy. Artificially driven Artificial Intelligence (AI) methods and their advancement make it difficult to trace synthetic media through deepfakes that closely approximate real speech, facial expressions, and body movements. Consequently, traditional methods of detecting these are losing the race because they cannot compete with the newly invented methods that are more advanced in comparison. This paper proposes a lightweight and scalable approach to deepfake video detection using Binarized Neural Networks (BNNs). We integrate BNNs with Convolutional Neural Networks (CNNs) and Multi-task Cascaded Convolutional Networks (MTCNN) to boost feature extraction and analysis while making sure that this is done at a computational efficiency, especially to be deployed in resource-constrained systems such as mobile and embedded devices. The binarization of network weights and activations naturally deals with the trade-off regarding detection accuracy and computational cost. Our approach introduces a practical solution for real-time deepfake detection, thus advancing toward more secure and trusted digital environments. Our proposed model has achieved an accuracy of 80%. © 2025 IEEE.