Copy-Move Forgery Detection of Medical Images Using Most Valuable Player Based Optimization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
D. Suganya ◽  
K. Thirunadana Sikamani ◽  
J. Sasikala
Keyword(s):  
Medical Images ◽  
Forgery Detection ◽  
Copy Move Forgery Detection

An Efficient Forensic Approach for Copy-move Forgery Detection via Discrete Wavelet Transform

2020 ◽  
Author(s):  
Rehan Ashraf ◽  
Muhammad Sheraz Mehmood ◽  
Toqeer Mahmood ◽  
Junaid Rashid ◽  
Muhammad Wasif Nisar ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
Copy Move Forgery Detection

Copy Move Forgery Detection based on double matching

2021 ◽  
Vol 76 ◽  
pp. 103057
Author(s):  
Qiyue Lyu ◽  
Junwei Luo ◽  
Ke Liu ◽  
Xiaolin Yin ◽  
Jiarui Liu ◽  
...  
Keyword(s):  
Forgery Detection ◽  
Copy Move Forgery Detection

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

Sign in / Sign up

Export Citation Format

Share Document