DETECTION OF FAKE COLORIZED IMAGES USING DENSE CONVOLUTION NETWORK
Author(s):
S. Aashmi Miranda , Dr. K. Thulasimani
Keywords:
image forgery, image detection, histogram, feature encoding, neural network.
Abstract
- Image forgery implies altering the digital image to some meaningful or valuable data. Image forensics is a well developed field that analyzes the images of specific conditions to build up trust and genuineness. Although image editing techniques can provide significant appreciation of the image or entertainment value, they may also be used with malicious intent. An emerging image editing technique is colorization, in which gray scale images are colorized with realistic colors. But this technique may also be intentionally applied to certain images to confound object recognition algorithms. In this work, it is observed that, colorized images, usually change the images using a variety of mechanisms. The digital image developed from the colorization Method possess statistical differences in their RGB channels, hue and saturation channels and also need to observe statistical inconsistencies in the dark and bright channels, because the colorization process will mainly affect the dark and bright channel values. Based on the experiments in the hue, saturation, dark and bright channels, two simple yet effective detection method Histogram based and Feature Encoding based Fake Colorized Image Detection along with the dense convolution network are proposed for detecting the fake colorized images
Article Details
Unique Paper ID: 150218

Publication Volume & Issue: Volume 7, Issue 4

Page(s): 104 - 110
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Last Date 25 August 2020

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