Title
An Image Splicing And Copy-Move Detection Method Based On Convolutional Neural Networks With Global Average Pooling
Abstract
Splicing and copy-move are two well-known methods of image tampering, while detection of image splicing and copy-move forgery is an important research topic in image forensics. In this paper, a method based on convolutional neural network with global average pooling was proposed for splicing and copy-move tampering detection. To detect image tampering, the inconsistency between the authentic images and the tampered images should be captured regardless of the image contents. So, the existing strategy using high-pass filter in SRM as initialization of the first layer was improved to reduce the influence of image content and make the features more diverse on each channel at the same time. In order to reduce the number of parameters in the fully connected layers and avoid overfitting, global average pooling was utilized before fully connected layers in the proposed model. Experiments on three public image tampering datasets demonstrated that the proposed method outperformed some state-of-the-art methods.
Year
DOI
Venue
2019
10.1007/978-3-030-34113-8_22
IMAGE AND GRAPHICS, ICIG 2019, PT III
Keywords
DocType
Volume
Image tampering detection, Image splicing, Copy-move, Convolutional neural networks (CNNs), SRM (Spatial Rich Model), Global average pooling
Conference
11903
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Qian Zhang129043.11
Jun Sang24012.62
Weiqun Wu321.04
Bin Cai431.43
Zhongyuan Wu512.05
Haibo Hu600.34