Identifying Plagiarized Images in Journal Submissions

Mark Gross | President, Data Conversion Laboratory (DCL)
Ari Gross | Chief Executive Officer, SoftWorks AI
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Plagiarism has become a major concern in the scholarly publishing world, and checking submitted text against article databases has become a routine practice for many publishers. However, identifying plagiarized images is more complicated, and not done. This webinar will discuss an approach we developed to compare submitted images against an image bank identifying those that are not just identical, but “similar”. We will discuss the types of alterations that need to be considered, and how to build a process fast enough to compare images on a large scale.

 

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about the presenter(s)

Mark Gross,
President, Data Conversion Laboratory (DCL)

Mark Gross, President, Data Conversion Laboratory, is a recognized authority on XML implementation and document conversion. Mark also serves as Project Executive, with overall responsibility for resource management and planning. Prior to joining DCL in 1981, Mark was with the consulting practice of Arthur Young & Co. Mark has a BS in Engineering from Columbia University and an MBA from New York University. He has also taught at the New York University Graduate School of Business, the New School, and Pace University. He is a frequent speaker on the topic of automated conversions to XML and SGML.

Ari Gross,
Chief Executive Officer, SoftWorks AI

Dr. Gross, Ph.D., received a BS degree in Mathematics from Johns Hopkins University and a Ph.D. in Computer Science from Columbia University. Dr. Gross has been very active in the research and development of new methods and technology in the areas of computer vision, imaging, artificial intelligence, machine learning, and real-time computing for the past 20 years. His achievements include over 40 published papers and several patents in areas related to imaging, machine learning, and document automation.