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@article{189262,
        author = {Damini Thakur and Rashmi Raman and P.N. Ramakrishnan},
        title = {Forensic Analysis of Device-Induced Motion Blur and Image Tampering Detection Using Amped FIVE Software},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {5183-5191},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189262},
        abstract = {In most modern camera devices, particularly those used for video surveillance such as CCTV systems and body-worn cameras, shutter speed is automatically controlled to adapt to varying lighting and environmental conditions. As a result, motion blur frequently occurs in surveillance footage, notably in cases involving moving vehicles, where critical details such as number plates and object boundaries become unclear. This poses significant challenges for forensic analysis and the reliability of digital evidence. For the experimental spade work, casually captured images of a parked vehicle taken using an unknown-pixel mobile camera were analyzed to simulate real-world challenges that arises majorly while examining the forensic cases. This study addresses the problem of motion blur caused by automatic shutter limitations by applying forensic image deblurring and enhancement techniques using Amped FIVE PROFESSIONAL software (Amped SRL) having Built date 20170302. Experimental results demonstrate that appropriate deblurring and enhancement workflows can substantially improve the interpretability of motion-blurred CCTV footage. Special attention was given to EXIF information, including camera model, camera aspect ratio, timestamp accuracy, geolocation entries, and other device-specific markers.},
        keywords = {Amped FIVE, CCTV footage, filters, metadata, motion blur.},
        month = {December},
        }