Preeti Yadav et al. / International Journal on Computer Science and Engineering (IJCSE) Detection of Copy-Move Forgery of Images Using Discrete Wavelet Transform (original) (raw)
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A Survey on Copy Move Image Forgery Detection Using Wavelet Transform
2015
Editing of images are very common these days. The process of creating fake images has been very simple with the introduction of powerful computer graphics. Such tempering with digital images is known as image forgery. With the advancement of the digital image processing software and editing tools, a digital image can be easily forged. The detection of image forgery is very important because an image can be used as legal evidence, in investigations, and in many other areas. The pixel-based image forgery detection aims to verify the authenticity of digital images without any prior knowledge of the original image. There are many different ways for tampering an image such as splicing or copy-move, resampling an image that are resize, rotate, stretch, addition and removal of any object from the image. In this we have discussed various pixel-based techniques for image forgery detection.
DWT Based Copy-Move Image Forgery Detection
International Journal of Advanced Research in Computer Science and Electronics Engineering, 2012
In an age with digital media, it is no longer true that seeing is believing.In addition, digital forgeries, can be indistinguishable from authentic photographs. In a copy-move image forgery, a part of an image is copied and then pasted on a different location within the same image .In this paper an improved algorithm based on Discrete Wavelet Transform (DWT)is used to detect such cloning forgery. In this technique DWT (Discrete Wavelet Transform) is applied to the input image to yield a reduced dimensional representation.After that compressed image is divided into overlapping blocks. These blocks are then sorted and duplicated blocks are identified. Due to DWT usage, detection is first carried out on lowest level image representation so this Copy-Move detection process increases accuracy of detection process.
Passive copy move image forgery detection using undecimated dyadic wavelet transform
2012
In this paper, a blind copy move image forgery detection method using undecimated dyadic wavelet transform (DyWT) is proposed. DyWT is shift invariant and therefore more suitable than discrete wavelet transform (DWT) for data analysis. First, the input image is decomposed into approximation (LL1) and detail (HH1) subbands. Then the LL1 and HH1 subbands are divided into overlapping blocks and the similarity between blocks is calculated. The key idea is that the similarity between the copied and moved blocks from the LL1 subband should be high, while that from the HH1 subband should be low due to noise inconsistency in the moved block. Therefore, pairs of blocks are sorted based on high similarity using the LL1 subband and high dissimilarity using the HH1 subband. Using thresholding, matched pairs are obtained from the sorted list as copied and moved blocks. Experimental results show the effectiveness of the proposed method over competitive methods using DWT and the LL1 or HH1 subbands only.
Copy-Move Forgery Detection Using Dyadic Wavelet Transform
2011
The issue of the verification of the authenticity and integrity of digital images is increasingly being important. Copy move forgery is one type of tempering that is commonly used for manipulating the digital contents; in this case, a part of an image is copied and is pasted on another region of the image. The non-intrusive approach for this problem is becoming attractive because it does not need any embedded information, but it is still far from being satisfactory. In this paper, an efficient non-intrusive method for copy-move forgery detection is presented. This method is based on image segmentation and similarity detection using dyadic wavelet transform (DyWT). Copied and pasted regions are structurally similar and this structural similarity is detected using DyWT and statistical measures. The results show that the proposed method outperforms the stat-of-theart methods.
Detection of Copy-Move Forgery Exploiting LBP Features with Discrete Wavelet Transform
International Journal of Computer Applications, 2016
Copy-move forgery is being used at various fields to hide significant information or to append additional information in image. Image forgery results in false interpretations. In this forgery, one section of image is copied and then it is pasted over the same image at different location. Although, various techniques are suggested by researchers but finding forged section of varying size and located at different locations on image is complicated. To resolve such problems we introduce a new hybrid approach for finding copy-move forgery based on Discrete Wavelet Transform with Local Binary Pattern. At First, image is moldered into three color components. Discrete Wavelet Transform is applied over the image which results in four sub bands. Approximation sub image contains low frequency components having maximum information. LL subimage is divided in overlapping blocks. Local Binary Pattern is calculated for blocks to generate descriptors to match similar blocks. Shift vectors are computed to find group of block pairs with similar shifting. It is observed by our experimental results that proposed method can efficiently detect manipulated images having different forgery size with high detection accuracy and low false positive rate as comparison to other state-of-the-art.
Blind copy move image forgery detection using dyadic undecimated wavelet transform
2011
In this paper, we propose a blind copy move image forgery detection method using dyadic wavelet transform (DyWT). DyWT is shift invariant and therefore more suitable than discrete wavelet transform (DWT) for data analysis. First we decompose the input image into approximation (LL1) and detail (HH1) subbands. Then we divide LL1 and HH1 subbands into overlapping blocks and measure the similarity between blocks. The key idea is that the similarity between the copied and moved blocks from the LL1 subband should be high, while the one from the HH1 subband should be low due to noise inconsistency in the moved block. We sort pairs of blocks based on high similarity using the LL1 subband and high dissimilarity using the HH1 subband. Using thresholding, we obtain matched pairs from the sorted list as copied and moved blocks. Experimental results show the effectiveness of the proposed method over competitive methods using DWT and the LL1 or HH1 subbands only.
An Approach to Detect Image Forgery by Discrete Wavelet Decomposition
Journal of the Institute of Engineering
Image forgery or manipulation by using the multimedia technology is becoming a challenging issue. The most common type of image forgery is copy-move forgery where some part of one image is copied and spliced in the other image. In this article, first the images in RGB color space is converted into YCbCr color space and the four-level discrete wavelet transform (DWT) is implemented to detect image forgery. The output of the DWT is further processed by using the image gradient technique for the edge detection of spliced objects. Morphological operation and Wiener filtering are applied for locating the tempered region in the forged image. Sensitivity, specificity and accuracy calculated for spliced images of CASIA datasets are obtained 89%, 86% and 88% respectively.
Copy-Move Forgery Detection using Orthogonal Wavelet Transforms
International Journal of Computer Applications, 2014
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 Copy-Move 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.
Dyadic wavelets and dct based blind copy-move image forgery detection
IET Conference on Image Processing (IPR 2012), 2012
This paper proposes a blind method of copy move image forgery detection using dyadic wavelet transform (DyWT) and discrete cosine transform (DCT). An input image is decomposed using DyWT to approximation (LL) subbands and detail (HH) subbands. DCT is then applied to overlapping blocks in LL and HH subbands, and Euclidean distances between the blocks are calculated using DCT coefficients. Decision is made based on similarity of the blocks in LL subband and dissimilarity of the blocks in HH subband. The proposed method is evaluated with images of different sizes, different compression qualities, and with or without rotation before pasting. Experimental results show that the method performs better in all cases than two other multiresolution based methods.
In the present digital world, digital images and videos are the main carrier of information. However, these sources of information can be easily tampered by using readily available software thus making authenticity and integrity of the digital images an important issue of concern. And in most of the cases copy-move image forgery is used to tamper the digital images. Therefore, as a solution to the aforementioned problem we are going to propose a unique method for copy-move forgery detection which can sustained various pre-processing attacks using a combination of Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT). In this process first DyWT is applied on a given image to decompose it into four parts LL, LH, HL, and HH. Since LL part contains most of the information, we intended to apply SIFT on LL part only to extract the key features and find a descriptor vector of these key features and then find similarities between various descriptors vector to conclude that there has been some copy-move tampering done to the given image. And by using DyWT with SIFT we are able to extract more numbers of key points that are matched and thus able to detect copy-move forgery more efficiently.