Copy-Move Forgery Detection using Orthogonal Wavelet Transforms

Abstract

With the help of various image editing tools available, it has become easier to alter an image in such a way that it does not leave behind any clues. Copy -Move forgery is a type of image forgery in which a part of digital image is copied and pasted to another part of same image. Since the copied and pasted image comes from the same image, it becomes difficult to detect the forgery. Generally the intention behind CopyMove forgery is to hide important objects in an image. In this paper, an orthogonal wavelet transform based forgery detection method is proposed. Orthogonal wavelet transform is generated from basic orthogonal transforms. We consider generating Discrete Cosine Transform Wavelet (DCTW) transform and Walsh Wavelet (WW) transform from DCT and Walsh orthogonal transforms. The image is divided into overlapping blocks. On each block, DCTW and WW transforms are applied. From each block discriminative features are extracted from coefficients. These feature vectors are lexicographically sorted and block matching step is applied to find duplicated blocks.

A Comprehensive Survey on Passive Techniques for Digital Image Forgery Detection

“…The cloned portions in the copy-move technique [19][20][21][22][23][24][25][26][27] have some traits in common with the remaining images, such as backdrop, lighting, and dynamic range. Forgery detection consequently gets more challenging.…”

“…Using block-based methods, a tiny overlapping block is used to extract feature vectors. In [20], the images are split up into overlapping blocks, and then Walsh Wavelet (WW) and Discrete Cosine Transform Wavelet (DCTW) are applied to each block. Discriminative characteristics are collected from coefficients for every block.…”

Efficient Forgery Detection Approaches for Digital Color Images

“…Similarity tools in statistics can be utilized with these techniques. Several studies have introduced a general survey [17][18][19][20][21][22][23][24][25] for different types of forgery techniques. In the copy-move technique [26][27][28][29][30][31][32][33][34], the copied parts share characteristics such as lighting conditions, background, and dynamic range with the rest of the images.…”

A Survey of Partition-Based Techniques for Copy-Move Forgery Detection

“…As to the method in [ 34 ], the components of the 7 resulting subbands are quantized to form the blocks feature vector. In [ 58 ], two orthogonal wavelet transforms are applied on each block, namely, the discrete cosine transform wavelet (DCTW) and the Walsh wavelet (WW). Further, for each block, a feature vector is built by computing the mean, the sum of the absolute values, the square root of the squared values' sum, the standard deviation, and the average residual of the pixels in the block.…”