Blind Approach for Digital Image Forgery Detection (original) (raw)
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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...
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.
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.
Review on Image Splicing Forgery Detection
With the growing usage of internet in daily life along with the usage of powerful image editing software tools in creating forged images effortlessly, making us to lose the trust in authenticity of the images. More than a decade an extensive research is going on in the field of Image forensics aims at restoring trustworthiness in images by bringing various tampering detection techniques. In this regard, we attempt to survey various techniques found particularly in Splicing Image Forgery Detection. We summarize both feature based as well as camera characteristics based techniques over the recent years.
IMAGE SPLICING DETECTION BASED ON DEMOSAICKING AND WAVELET TRANSFORMATION
Image splicing is a form of digital image manipulation by combining two or more image into a new image. The application was developed through a passive approach using demosaicking and wavelet transformation method. This research purposed a method to implement the demosaicking and wavelet transform for digital image forgery detection with a passive approach. This research shows that (1) demosaicking can be used as a comparison image in forgery detection; (2) the application of demosaicking and wavelet transformation can improve the quality of the input image (3) demosaicking and wavelet algorithm are able to estimate whether the input image is real or fake image with a passive approach and estimate the manipulation area from the input image.
Review of Image Splicing Forgeries
2017
In Today’s world modification of images has become easy due to availability of various powerful software tools and applications which allows manipulation of the images and makes it to appear real. The image forgery is practiced in most of the fields such as manipulation of important government documents, wills, financial deeds and educational certificates etc. When the forged images are produced as a proof in a court room that might derive wrong decisions due to illegitimate. Hence checking the genuineness and authenticity of images is one of the most important and active research areas in digital image forensics. In this survey paper we attempt to provide an overview of different approaches for detecting image splicing forgeries.
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...
A Robust Algorithm of Forgery Detection in Copy-Move and Spliced Images
International Journal of Advanced Computer Science and Applications, 2016
The paper presents a new method to detect forgery by copy-move, splicing or both in the same image. Multiscale, which limits the computational complexity, is used to check if there is any counterfeit in the image. By applying one-level Discrete Wavelet Transform, the sharped edges, which are traces of cut-paste manipulation, are high frequencies and detected from LH, HL and HH sub-bands. A threshold is proposed to filter the suspicious edges and the morphological operation is applied to reconstruct the boundaries of forged regions. If there is no shape produced by dilation or no highlight sharped edges, the image is not faked. In case of forgery image, if a region at the other position is similar to the defined region in the image, a copy-move is confirmed. If not, a splicing is detected. The suspicious region is extracted the feature using Run Difference Method (RDM) and a feature vector is created. Searching regions having the same feature vector is called detection phase. The algorithm applying multiscale and morphological operation to detect the sharped edges and RDM to extract the image features is simulated in Matlab with high efficiency not only in the copymove or spliced images but also the image with both copy-move and splicing.
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.
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.