An effective image copy-move forgery detection using entropy image
Zhaowei Lu Li Jiang
Image forensics has become increasingly important in our daily lives. As a
fundamental type of forgeries, Copy-Move Forgery Detection (CMFD) has received
significant attention in the academic community. Keypoint-based algorithms,
particularly those based on SIFT, have achieved good results in CMFD. However,
the most of keypoint detection algorithms often fail to generate sufficient
matches when tampered patches are present in smooth areas. To tackle this
problem, we introduce entropy images to determine the coordinates and scales of
keypoints, resulting significantly increasing the number of keypoints.
Furthermore, we develop an entropy level clustering algorithm to avoid
increased matching complexity caused by non-ideal distribution of grayscale
values in keypoints. Experimental results demonstrate that our algorithm
achieves a good balance between performance and time efficiency.