@techreport{TR-IC-PFG-19-53,
   number = {IC-PFG-19-53},
   author = {João {Phillipe Cardenuto} and Anderson {Rocha}},
   title  =  {{Scientific Integrity - Analysis of Misconduct in Images
                   of Scientific Papers}},
   month = {December},
   year = {2019},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 32 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       The  pressure  of  ``publish  or  perish"  in  the  competitive 
       research  environment  of  science  leads  many  scientists  to 
       misconduct.  Aiming  to  foster scientific integrity, this work
       proposes   a  framework  for  detecting  suspicious  images  of 
       scientific  articles.  Its workflow begins with a PDF file of a
       scientific  publication and ends with highlighting of suspected
       fraud  regions.  This  workflow  is divided into four operation
       steps:  image  extraction,  image segmentation, clustering, and
       copy-move  forgery  detection. Each module of the framework was
       validated   individually.   As  a  result,  the  framework  has 
       outperformed  existing methods for accomplishing each task. The
       image  extraction  achieves  better  results  on efficiency and
       effectiveness   than   famous   extraction  images  tools  (e.g 
       pdfimages);   the   segmentation   achieves  98\%  accuracy  on 
       detecting  relevant  images  regions  and  a proposed fusion of
       copy-move  forgery  detection  achieves the best result of 19\%
       average IoU on a dataset with 100 images proposed by this work.
       In  addition,  a  real  case  of fraud was used to validate the
       framework  as  a whole. The images highlighted by the framework
       were the same as described in the case's retraction note.
  }
}