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  Published Paper Details:

  Paper Title

FAKE IMAGE DETECTION USING MACHINE LEARNING

  Authors

  Ravi Shankar,  Akshat Srivastava,  Gurunath Gupta,  R.B.Jadhav,  Umesh Thorat

  Keywords

Fake Image Detection

  Abstract


These days, the availability of image processing software, such as Adobe Photoshop or GIMP have made image manipulation so common. Detecting such fake images is unavoidable for unveiling of the image-based cybercrimes. An image taken by digital camera or smartphone is usually saved in the JPEG format due to its popularity. JPEG algorithm works on image grids, compressed independently, with a size of 8x8 pixels. While unmodified images, have a similar error level. For resaving operation, each block should degrade at around same rate due to similar amount of errors across the whole image. The compression ratio of this fake image is different from that of the original image and is detected using Error Level Analysis. The objective of our paper is to develop a photo forensics algorithm which can detect any photo manipulation. The error level analysis was then enhanced using vertical and horizontal histograms of error level analysis image to pinpoint the location of modification. Results show that the proposed algorithm could identify the modified image while showing the exact location of modifications.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2005044

  Paper ID - 194271

  Page Number(s) - 295-302

  Pubished in - Volume 8 | Issue 5 | May 2020

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Ravi Shankar,  Akshat Srivastava,  Gurunath Gupta,  R.B.Jadhav,  Umesh Thorat ,   "FAKE IMAGE DETECTION USING MACHINE LEARNING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 5, pp.295-302, May 2020, Available at :http://www.ijcrt.org/papers/IJCRT2005044.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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