What is it about?
Advanced image capturing devices like digital cameras have increased the possibility of recapturing high-quality images from output media. This research was aimed at developing a machine learning model that can detect recaptured images by analyzing the subtle texture and illumination feature pattern differences between original scene and recaptured images.
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Photo by Mika Baumeister on Unsplash
Why is it important?
Images can be acquired illegally by recapturing them from output media. Some of these acquired images tend to be personal images and when acquired, they be illegally shared and redistributed without the owner's consent which violates data privacy and ownership principles. In addition, these images can be used in attacks such image spoofing attacks to in attempt to gain unauthorized access to information systems. Therefore, detection of recaptured images is of fundamental importance as far as privacy and information protection is concerned.
Perspectives
With the rapidly advancing digital image technology, I believe this article is timely as many people constantly share/forward images without considering the authenticity of the image source. Detecting whether the image is originally captured or recaptured from some display device can help to make informative decisions.
Hassan Wasswa
Texas Tech University
Read the Original
This page is a summary of: Recaptured Image Forensics Based on Image Illumination and Texture Features, December 2020, ACM (Association for Computing Machinery),
DOI: 10.1145/3447450.3447465.
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