A Robust Algorithm of Forgery Detection in Copy-Move and Spliced Images (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.
Digital Image Forgery Detection using Wavelet Decomposition and Edge Detection
In the presented work, a copy-paste or cut-paste detection algorithm is discussed. The algorithm is based on wavelet decomposition of the input image and then extracting the edges so that the cut-paste or copy-paste region is detected by examining the edge pixels in wavelet domain. The scheme consists of: image acquisition, gray scale conversion, wavelet decomposition using haar wavelet, edge detection using sobel filter and then analysis of the region falling under the straight edge chain. The cut-paste or copy-paste region is normally in rectangular or square edge region. The edge pixels are analyse for their chain code so that the straight line could be extracted from the pixel tracing. This gives the possible region of image forgery. Further, the input image and forged images are compared with respect to different properties like entropy, power energy, standard deviation etc and re discussed in the algorithm.
Security and Communication Networks, 2021
With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms...
2014
Abstract-Digital images are used everywhere and it is easy to manipulate and edit because of availability of various image processing and editing software. In a copy-move image forgery, a part of an image is copied and then pasted on a different location within the same image. A copy-move image forgery is done either for hiding some image object, or adding more details resulting in at least some part being cloned. In both the case, image reliability is lost. In this paper an improved algorithm based on Discrete Wavelet Transform (DWT) is used to detect such cloning forgery. In this technique at first DWT (Discrete Wavelet Transform) is applied to the input image for a reduced dimensional representation. Then the compressed image is divided into overlapping blocks. After that Lexicographic sorting is performed, and duplicated blocks are identified. Due to DWT usage, detection is first carried out on lowest level image representation. This approach increases accuracy of detection proc...
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.
Forgery Detection in Digital Image
2013
Due to availability of many image editing and processing tools, it is possible to easily change the information represented by a digital image without leaving any obvious traces of tampering. Tampering of digital images has become so easy that it is raises question about integrity/authenticity of digital image, so there is a need of a robust and reliable forgery detection method. A specific type of forgery in digital image is known as copy move forgery detection. This is done by copying a block of an image and pasting it on to some other block of the same image. This approach is based on the application of wavelet transform. To achieve this, first apply wavelet transform to the input image then reduced dimensions of image is divided into overlapping block of fixed size and duplicated blocks are identified using phase correlation.
An integrated technique for splicing and copy-move forgery image detection
2011 4th International Congress on Image and Signal Processing, 2011
Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.
A Closure Looks to Copy Move Forgery Detection Techniques for Digital Images
In the past ten years, image forgery detection has been emerged as a remarkable research in applications of computer vision, digital image processing, biomedical technology, criminal investigation, image forensics, etc. It becomes more attractive and challenging when powerful software tools for image processing are so popular and sophisticated that we cannot confirm whether an image is manipulated by naked eyes. Image forgery detection is one kind of passive techniques using blind algorithms to detect traces of tampering in a given image without prior information or security codes. The images can be forged by splicing details from itself, which is called Copy-Move images, or from the other images called spliced images. In this paper a comprehensive review of different techniques of image forgery such as PCA, discrete wavelet transform, segmentation based CMFD etc. has been discussed.
Passive Detection of Copy-Move Forgery using Wavelet Transforms and SIFT Features
Image forgery is a major issue today in publishing and printing. Several images are morphed before publishing in order to incorporate extra information. The problem becomes more complicated with different means of image capture. There exist a variety of cameras with different resolutions and encoding techniques. Many times the forged image is compressed or resized before publishing. Detecting forgery in such cases is a challenging task. Different techniques of tampering include resizing, blurring, compression, addition of noise, image splicing etc. The most common type of digital image forgery is copy-move forgery. In this paper, a passive technique for detecting copy-move forgery based on wavelet transforms and SIFT features is proposed. The wavelet transforms employed are Discrete Wavelet Transform (DWT) and Dyadic Wavelet Transform (DyWT). The image is divided into four sub-bands viz. LL, LH, HL and HH by the wavelet transform. Since the LL sub-band contains most of the information, SIFT is applied on the LL part only, to extract the key features and find descriptor vector of these key features and then find similarities between various descriptor vectors to conclude that the given image is forged.
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.