BibTeX:

@article{IJIRSTV2I9073,
     title={A Novel Approach for Contrast Enhancement-Based Forensics in Digital Images},
     author={Kalyani Ravindra Chintawar, NA, NA, NA and NA},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={2},
     number={9},
     pages={320--323},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV2I9073.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

The contrast enhancement technique is specifically used to adjust the global brightness and contrast of images. Malicious users may perform local contrast enhancement for creating realistic composite images. In project, novel Approach for Contrast Enhancement Detection is used to detect the contrast enhancement in the images for verifying the originality and authenticity of the images. The new methodology involves two novel algorithms to detect the manipulations of contrast enhancement in digital images. One of the algorithms focuses on the detection of global contrast enhancement applied to the JPEG-compressed images, as these are widespread in real applications. The histogram artifacts incurred by the JPEG compression and also the pixel value mappings are analyzed theoretically and then distinguished by identifying the zero-height gap bins. Another algorithm helps to identify the composite image which is created by enforcing contrast adjustment on either single source region or both source regions. Then the positions of detected block wise peak/gap bins are clustered to recognize the contrast enhancement mappings which are applied to different source regions. At last, the consistency between regional artifacts is checked for discovering the image forgeries and also for locating the composition boundary. Matlab tool is used for evaluating the performance of these two algorithms on various images. The extensive experiment also verified the effectiveness and efficacy of the new methodology.

JPEG compressed image, Histogram Artifacts, Peak/Gap bins, Contrast Enhancement, Contrast Detection