A copy-move image forgery detection technique based on tetrolet transform

Copy-move forgery is a common type of forgery in digital images. In copy-move forgery, one part of the image is replicated within the same image, generally at different location. For revival of trustworthiness of images, there is a need to develop an efficient and robust technique to detect such forgeries. This paper proposes a new copy-move image forgery detection technique based on Tetrolet transform. In this technique, initially the input image is divided into overlapping blocks, then four low-pass coefficients and twelve high-pass coefficients are extracted from each block by applying Tetrolet transform. Feature vectors are then sorted lexicographically, and similar blocks are identified by matching the extracted Tetrolet features. Experimental results show that the proposed technique can detect and locate the duplicated regions in the images very accurately, even when the copied regions have undergone some post-processing operations blurring, color reduction, adjustment of brightness and contrast, rotation, scaling, JPEG compression. In addition, it is also observed that the proposed technique is able to detect very small duplicated regions and multiple forgery cases, even when image is smooth.