TY - GEN
T1 - Image fakery and neural network based detection
AU - Lu, Wei
AU - Chung, Fu Lai Korris
AU - Lu, Hongtao
PY - 2006/1/1
Y1 - 2006/1/1
N2 - By right of the great convenience of computer graphics and digital imaging, it is much easier to alter the content of an image than before without any visually traces. Human has not believed what they see. Many digital images can not be judged whether they are real or feigned visually, i.e., many fake images are produced whose content is feigned. In this paper, firstly, image fakery is introduced, including how to produce fake images and its characters. Then, a fake image detection scheme is proposed, which uses radial basis function (RBF) neural network as a detector to make a binary decision on whether an image is fake or real. The experimental results also demonstrated the effectiveness of the proposed scheme.
AB - By right of the great convenience of computer graphics and digital imaging, it is much easier to alter the content of an image than before without any visually traces. Human has not believed what they see. Many digital images can not be judged whether they are real or feigned visually, i.e., many fake images are produced whose content is feigned. In this paper, firstly, image fakery is introduced, including how to produce fake images and its characters. Then, a fake image detection scheme is proposed, which uses radial basis function (RBF) neural network as a detector to make a binary decision on whether an image is fake or real. The experimental results also demonstrated the effectiveness of the proposed scheme.
UR - http://www.scopus.com/inward/record.url?scp=33745897296&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
SN - 3540344373
SN - 9783540344377
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 610
EP - 615
BT - Advances in Neural Networks - ISNN 2006
PB - Springer Verlag
T2 - 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Y2 - 28 May 2006 through 1 June 2006
ER -