Abstract
With rapid development of the Internet, images are spreading more and more quickly and widely. The phenomenon of image illegal usage emerges frequently, and this has marked impacts on people’s normal life. Therefore, it is of great importance to protect image security and image owner’s rights. At present, most image protection is passive. Most of the time, only when the images had been used illegally and serious adverse consequences had appeared did the image owners discover it. In this paper, a Spark-based real-time proactive image tracking protection model (SRPITP) is proposed to monitor the status of images under protection in real time. Whenever illegal use is found, an alert will be issued to image owners. The model mainly includes image fingerprint extraction module, image crawling module, and image matching module. The experimental results show that in SRPITP, the image matching accuracy rate is above 98.9%, and compared with its stand-alone counterpart, the corresponding time reduction for image extraction and matching are about 58.78% and 61.67%.
Highlights
No one would like others to use his belongings unauthorized, especially photos or other images
Mary was very angry about the illegal use of her photo, and she wanted to know when this unauthorized use began
(3) The fingerprints of these images and relevant information of the owner are inserted into the protected image database
Summary
No one would like others to use his belongings unauthorized, especially photos or other images. There have been a lot of research results in image processing, while to the best of our knowledge, there is no research on real-time and proactive image tracking protection model till now. A Spark-based real-time proactive image tracking protection model (SRPITP) is proposed to find out the illegal use of images and protect the image owners’ legitimate rights. This model is deployed in the parallel computing frame Spark to improve the system’s real-time performance. To deal with massive scene images retrieval, [27] puts forward an improved K-means feature clustering-based system and Hadoop is chosen as the parallel computing framework. (3) The fingerprints of these images and relevant information of the owner are inserted into the protected image database
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