Results 71 to 80 of about 9,242 (179)

Autoencoder with recurrent neural networks for video forgery detection

, 2017
Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos.
Cozzolino, Davide, D'Avino, Dario, Poggi, Giovanni, Verdoliva, Luisa   +3 more
core   +1 more source

Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

, 2018
Computer-generated graphics (CGs) are images generated by computer software. The~rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish ...
Hu, Weitong, Shi, Yun-Qing, Wu, Ting, Yao, Ye, Zhang, Wei   +4 more
core   +2 more sources

Overcoming Natural Similarity: A DenseNet-Based Approach for Reliable Transform Copy-Move Forgery Identification

IEEE Access
Digital image forensics aims to verify the authenticity of an image by determining its origin and any processing it has undergone. Among passive approaches in copy move forgery detection, which analyze images without requiring prior embedded information,
Tanzeela Qazi, Mohsin Shah, Mushtaq Ali, Muhammad Nawaz Khan, Ahmad Ali Alzubi, Abeer Alnuaim, Tariq Hussain   +6 more
doaj   +1 more source

Random Forest based Copy-Move Forgery Detection

, 2020
Proving the authenticity of images is animportant part of image forensics. Copy-move forgery is amethod of forgery commonly followed in blind image forensics.We propose the use of a modified Auto Color Correlogram toobtain feature vectors from the forged image.
openaire   +2 more sources

Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics. [PDF]

J Imaging, 2021
Rodriguez-Ortega Y, Ballesteros DM, Renza D.   +2 more
europepmc   +1 more source

An Efficiency Enhanced Cluster Expanding Block Algorithm for Copy-Move Forgery Detection [PDF]

[[conferencetype]]國際[[conferencetkucampus]]台北校園[[conferencedate]]20150902~20150904[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Taipei ...
[[corresponding]]Chen, Chien-Chang, Lin, Cheng-Shian   +1 more
core  

ECMNet: A deep framework for copy move forgery localization detection and classification

Results in Engineering
Copy-move forgery is one of the most prevalent forms of digital image forgery. It violates the authenticity of digital images in the context of documentation, judicial reports, social media, military operations, journalism, intelligence, and so on ...
Anjani Kumar Rai, Subodh Srivastava
doaj   +1 more source

The PS-Battles Dataset - an Image Collection for Image Manipulation Detection

, 2018
The boost of available digital media has led to a significant increase in derivative work. With tools for manipulating objects becoming more and more mature, it can be very difficult to determine whether one piece of media was derived from another one or
Heller, Silvan, Rossetto, Luca, Schuldt, Heiko   +2 more
core  

FI-SURF algorithm for image copy-flip-move forgery detection

Tongxin xuebao, 2015
FI-SURF (flip invariant SURF) algorithm was proposed,for the consideration of the copy-move forgery detec-tion of digital images.The arrangement of the SURF (speeded-up robust features) descriptor after image flip was studied.After extract the SURF ...
IYan L, IUNian L, HANGBin Z, UANKai-guo Y, ANGYi-xian Y   +4 more
doaj   +2 more sources

Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction

IEEE Access
In the development of image forensics, detection of Copy-Move Forgery (CMF) has become a major challenge due to the proliferation of image forgery techniques.
Varun Shinde, Vineet Dhanawat, Ahmad Almogren, Anjanava Biswas, Muhammad Bilal, Rizwan Ali Naqvi, Ateeq Ur Rehman   +6 more
doaj   +1 more source