Smart Computing and Systems Engineering - 2025 (SCSE 2025)

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    Deepfake Detection Using a Hybrid Deep Learning Approach with Swin Transformers and ConvNeXt
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Amarasinghe, H. M. S.; Kasthuri Arachchi, S. P.
    The rapid advancement of artificial intelligence has led to the proliferation of deep-fake technology, which poses significant challenges to digital security and trust. As deepfakes become increasingly sophisticated, there is an urgent need for effective detection methods that can accurately identify manipulated media across various platforms and contexts. Deepfakes are outcomes of advanced Artificial Intelligence algorithms such as Generative Adversarial Networks, and have become a danger to digital integrity, personal privacy, and public trust. With continuous advancements in the techniques for generating deepfakes, conventional methods of detection based on the artifact analysis of visuals and inconsistencies in physiological signals have started to wear out. This paper presents a hybrid deep learning model that works between Swin Transformers and ConvNeXt architectures for better detection. The proposed model achieves better detection accuracy and robustness by leveraging Swin Transformers' hierarchical feature extraction capabilities and the efficient processing strengths of ConvNeXt. Obtained results on the "Deepfake and Real Images" dataset demonstrated performance of 95.6% accuracy, 97.4% precision, 93.7% recall, and a 95.2% F1 score. The hybrid model is more effective than existing ones, demonstrating their potential for concrete application in social media platforms, news agencies, and digital forensics to help fight misinformation and preserve digital trust.
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    Virtually restoring headless Buddha statues using Deep Learning Techniques
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Liyanage, M. M.; Induka, L. P. D. P.; Dissanayake, P. P.; Wijeywickrama, W. K. D. A.; Kulathilake, K. A. S. H.
    Buddha statues are undoubtedly important for culture and history worldwide, particularly in Sri Lanka, where Buddhism is the major religion and represents the country’s rich historical heritage. Many historical Buddha statues from the earliest eras, such as those in Anuradhapura, have been destroyed or degraded due to natural disasters, vandalism, or aging. It is common for body parts, especially the head, to be lost or damaged in these statues. While various physical restoration methods exist, they are often expensive and may reduce the historical or artistic value of the statues. Virtual restoration methods, on the other hand, offer a non-invasive and affordable solution for preserving and reconstructing these heritage assets. This study presents a specially designed deep learning model using a U-Net architecture to segment the body of seated headless Buddha statues by removing the background from input images, trained on a custom dataset of Buddha statues found in Anuradhapura, Sri Lanka. These segmented images eliminate unnecessary details and provide a clean statue body for subsequent restoration tasks. The head inpainting phase, based on the segmented image, will be carried out using a GAN-based architecture, an extension of Pix2Pix, which virtually inpaints a matching head for the given statues. This two-stage approach—segmentation followed by inpainting—provides a complete pipeline for the virtual restoration of headless Buddha statues, maintaining cultural integrity while minimizing physical intervention. The proposed methodology demonstrates efficiency and serves as a valuable tool in heritage conservation and the virtual reconstruction of culturally significant artifacts.