Open Access
ARTICLE
Digital Forensics for Recoloring via Convolutional Neural Network
Zhangyi Shen1, Feng Ding2, *, Yunqing Shi1
1 Electrical and Computer Engineering Department, New Jersey Institute of Technology, 323 Martin Luther
King BLVD, Newark NJ 07102, USA.
2 Computer Science Department, State University of New York Albany, 1400 Washington Ave, Albany NY
12222, USA.
* Corresponding Author: Feng Ding. Email: .
Computers, Materials & Continua 2020, 62(1), 1-16. https://doi.org/10.32604/cmc.2020.08291
Abstract
As a common medium in our daily life, images are important for most people
to gather information. There are also people who edit or even tamper images to
deliberately deliver false information under different purposes. Thus, in digital forensics,
it is necessary to understand the manipulating history of images. That requires to verify
all possible manipulations applied to images. Among all the image editing manipulations,
recoloring is widely used to adjust or repaint the colors in images. The color information
is an important visual information that image can deliver. Thus, it is necessary to
guarantee the correctness of color in digital forensics. On the other hand, many image
retouching or editing applications or software are equipped with recoloring function. This
enables ordinary people without expertise of image processing to apply recoloring for
images. Hence, in order to secure the color information of images, in this paper, a
recoloring detection method is proposed. The method is based on convolutional neural
network which is quite popular in recent years. Unlike the traditional linear classifier, the
proposed method can be employed for binary classification as well as multiple labels
classification. The classification performance of different structure for the proposed
architecture is also investigated in this paper.
Keywords
Cite This Article
Z. Shen, F. Ding and Y. Shi, "Digital forensics for recoloring via convolutional neural network,"
Computers, Materials & Continua, vol. 62, no.1, pp. 1–16, 2020.
Citations