An image can be easily tampered given the development of digital imaging technologies nowadays. Copy-move forgery is one of the most commonly and easily used tampering techniques. To make the tampered image look normal

the copied region may be subjected to various post-processing operations. However

most existing methods for detecting altered regions are too sensitive to the post-processing operations. As most existing copy-move forgery detection algorithms are weak

a detection algorithm based on exponential Fourier moments is proposed in this paper. First

a grayscale image is divided into multiple overlapping blocks. Then

the exponential Fourier moments of every block will be regard as a feature vector

and all vectors are sorted by lexicographic sorting. The questionable blocks are selected based on vector similarity and block displacement. Finally

the error similar blocks are removed by the neighbors' number and the angles' variance to locate the final tampered region. If an RGB image is detected

then each color channel can be independently processed to obtain three results

and the final result is obtained by performing an “and” operation. Most existing copy-move forgery detection methods usually convert an RGB image into a grayscale image

thereby leading to information loss. As a result

we detect each color channel to have three independent outcomes and integrate the different results. To make the method more robust

we use the consistency of the pasted region to remove error similar blocks. Experimental results show that the method is robust against post-processing operations

such as rotation

noise addition

and Gaussian blur. Compared with the method that uses radial harmonic Fourier moments

our proposed method has a better efficiency when the noise is added to the image. The detection rate increased by about 26.66%

and the error rate decreased by about 33.77%. A user can easily creative a convincing image by copying and pasting content within the same image. The proposed method can detect and locate the duplicated regions. Moreover

the method is still effective even when an image is distorted by rotation

additive noise

or Gaussian blurring.