Open Access

ARTICLE

Digital Forensics for Recoloring via Convolutional Neural Network

Zhangyi Shen¹, Feng Ding^{2, *}, 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.

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Citations