TY - GEN
T1 - An Secure and Effective Copy Move Detection Based on Pretrained Model
AU - Hammad, Baraa Tareq
AU - Ahmed, Ismail Taha
AU - Jamil, Norziana
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The amount of counterfeit or faked photographs that depict inaccurate or incorrect information has increased. Hence, the Digital image forgery has become a serious problem. Copy move forgery is more risky since it involves copying and pasting a portion of an image into another region of the same image to hide information. Conventional Copy Move Forgery Detection (C-MFD) approaches have limitations in terms of performance. The explanation for this is that the discriminative ability and partially invariant to specific transformations of manual-crafted features are insufficient. Therefore, in this paper we used the AlexNet deep learning model to extract the image features and applies the ReliefF feature selection algorithm to get effective features. The logistic classifier is then given selected features to determine whether the image is forged or not. On the publicly accessible benchmark datasets MICC-F600 and MICC-F2000, the proposed method is tested. The precision rate of the presented method based on AlexNet model is equal to 94 %.
AB - The amount of counterfeit or faked photographs that depict inaccurate or incorrect information has increased. Hence, the Digital image forgery has become a serious problem. Copy move forgery is more risky since it involves copying and pasting a portion of an image into another region of the same image to hide information. Conventional Copy Move Forgery Detection (C-MFD) approaches have limitations in terms of performance. The explanation for this is that the discriminative ability and partially invariant to specific transformations of manual-crafted features are insufficient. Therefore, in this paper we used the AlexNet deep learning model to extract the image features and applies the ReliefF feature selection algorithm to get effective features. The logistic classifier is then given selected features to determine whether the image is forged or not. On the publicly accessible benchmark datasets MICC-F600 and MICC-F2000, the proposed method is tested. The precision rate of the presented method based on AlexNet model is equal to 94 %.
KW - AlexNet model
KW - Copy Move Forgery Detection (C-MFD)
KW - MICC-F600 and MICC-F2000
KW - ReliefF feature selection
KW - logistic classifier
UR - https://www.scopus.com/pages/publications/85137142248
UR - https://www.scopus.com/pages/publications/85137142248#tab=citedBy
U2 - 10.1109/ICSGRC55096.2022.9845141
DO - 10.1109/ICSGRC55096.2022.9845141
M3 - Conference contribution
AN - SCOPUS:85137142248
T3 - 2022 IEEE 13th Control and System Graduate Research Colloquium, ICSGRC 2022 - Conference Proceedings
SP - 66
EP - 70
BT - 2022 IEEE 13th Control and System Graduate Research Colloquium, ICSGRC 2022 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE Control and System Graduate Research Colloquium, ICSGRC 2022
Y2 - 23 July 2022
ER -