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There has been a wide development in the zone of advanced picture usage. One of the fundamental problems in this present reality is to judge the genuineness of a particular picture. These days it is anything but difficult to alter and manufacture computerized picture with the progression of the capable advanced picture handling programming and simp...
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There has been a wide development in the zone of advanced picture usage. One of the fundamental problems in this present reality is to judge the genuineness of a particular picture. These days it is anything but difficult to alter and manufacture computerized picture with the progression of the capable advanced picture handling programming and simp...
Citations
... LBP, along with SVD, is used by Wang et al. [153] to detect CMFD. In [59], LBP with the DWT and PCA is used to detect CMF. First, the image is converted from RGB to YCbCr, and DWT is applied. ...
... The mask was not given for the manipulated photos in either dataset (CASIA v1.0 or CASIA v2.0). Later studies by Zhang et al. [60] and Salloum et al. [59] have demonstrated that by comparing altered image with its original counterpart the masks can be obtained. Table 4 shows the description of various publicly available datasets used frequently by the researchers for CMFD. ...
In this digital era, a huge amount of images are flooding the internet, which is extensively used for digital communications, and are also regarded as a significant source of information in many fields. However, the images can be easily altered without leaving any traces due to the availability of digital image editing softwares. Hence, it becomes essential to validate the integrity of the images. One of the most serious and popular tampering procedures is Copy Move Forgery (CMF), wherein some portion of an image is copied and pasted to another region in the same image. This paper reviews recent state-of-the-art copy-move forgery detection (CMFD) schemes along with their pros, and cons with the help of tables for better readability. In addition, this paper enlists the performance evaluation criteria and different image datasets used for CMFD, along with their merits and demerits. At last, this review addresses the various issues, challenges, and future directions in the field of CMFD. This survey paper aims to provide researchers with a broad perspective on the various aspects of advancements in CMFD techniques.
... [53]. (Image resource from [53] Also using LBP, DCT and SVM, M. F. Jwaid et al. [54] made some improvements and added other methods. First, change the picture from RGB to YCbCr by applying preprocessing. ...
... According to their results, the proposed method is superior to existing methods on different well-known publicly available benchmark data sets for image forgery detection. Table 3 show the summary of above methods, in the feature extraction step, we can clearly understand the different between the two methods, Alahmadi, A. et al. [53] using LBP first and then using DCT, while M. F. Jwaid et al. [54] using DCT first and then using LBP, beside this, M. F. Jwaid et al. [54] using PCA before SVM, the purpose of this step is to determine whether it is necessary to perform SVM classification. The PCA method compares the values of two images (reference image and target image). ...
... According to their results, the proposed method is superior to existing methods on different well-known publicly available benchmark data sets for image forgery detection. Table 3 show the summary of above methods, in the feature extraction step, we can clearly understand the different between the two methods, Alahmadi, A. et al. [53] using LBP first and then using DCT, while M. F. Jwaid et al. [54] using DCT first and then using LBP, beside this, M. F. Jwaid et al. [54] using PCA before SVM, the purpose of this step is to determine whether it is necessary to perform SVM classification. The PCA method compares the values of two images (reference image and target image). ...
With the advancement of technology, new problems and challenges also follow, among which the more serious problem is media forgery. Forged media information brings a lot of inconvenience to life. It is difficult to distinguish the truth from the false. In this article, various types of forgery are listed and elaborated with a focus on the copy-move forgery category, the study and research involving copy-move forgery(CMF) detection techniques using the Local Binary Pattern (LBP) are presented. The technical review of recent state-of-the-art LBP-based is provided.
... Jwaid et al. [56] used DWTs and PCAs to do productive calculations in light of LBPs. Preprocess the image to convert it from RGBs to YCbCrs (Yellow, Green, and Blue). ...
Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness. These methods have identified forgery and its type and compared it with state of the art. This work will help us to find the best forgery detection technique based on the different environments. It also shows the current issues in other methods, which can help researchers find future scope for further research in this field.
... Generally, preprocessed information will usually make the following CMFD processes more efficient, resulting in faster detection speed or higher detection accuracy. Some examples of the preprocessing techniques are conversion of RGB to grayscale [6], [7], [8], HSV [9], [10] or YCbCr [11], [12], [13] color space, local binary pattern (LBP) [14], median filter [9], [15], discrete wavelet transform (DWT) [16], [17], and principal component analysis (PCA) [18]. In this paper, not only conversion and dimensionality reduction techniques, but also block division and segmentation are also included in preprocessing (e.g., SLIC [15], [19]). ...
... Recently, the idea of LBP has become more attractive in the field of CMFD. Some recent examples utilizing LBP as a feature description technique to detect CMF are proposed in [45], [46], [16], [47], [48]. Mahmood et al. [45], presents a new CMFD technique using stationary wavelets [39] together with LBP variance to detect anomalies within the digital image. ...
... This technique shows an interesting way to create feature descriptors from the target image block using a special vector containing sign information of the SVD coefficients computed from a previously processed LBP labeled image. Jwaid and Baraskar [16], in 2017, proposed a method to detect CMF using LBP with DWT and PCA. This technique uses LBP as a core feature extraction mechanism. ...
This paper presents the state-of-the-art technical reviews and analysis of recent copy-move forgery detection (CMFD) techniques. A new CMFD process pipeline was introduced. In addition, the techniques used in each stage of the CMFD pipeline are summarized and classified into small categories. Furthermore, the tables of comparison are provided as a quick reference. This technical review paper is expected to help provide useful insights and updated information regarding recent advancements in CMFD to researchers in the field.
Although image processing and forensic computing are different fields, they have been involved in the same computer science research areas such as image forgery detection, in recent years. Image forgery detection is a new branch of image processing due to increased image manipulation tools. Thus, we proposed a new block-based image forgery detection method within this framework. In this research, we applied the latest and easiest application feature extraction method used in a new iris recognition system, called rotation invariant neighborhood-based binary pattern, on the block-based image forgery detection system. To the best of our knowledge, this is the first work that applies to block-based copy-move forgery detection systems. The proposed method has been evaluated for different block sizes on a well-known image database (CoMoFod) in the literature. Experimental studies showed that our method forgery detection accuracy rate incentive results are higher than the state-of-the-art block-based forgery detection methods.
Copy-move forgery is one of the most common image tampering schemes, with the potential use for misleading the opinion of the general public. Keypoint-based detection methods exhibit remarkable performance in terms of computational cost and robustness. However, these methods are difficult to effectively deal with the cases when 1) forgery only involves small or smooth regions, 2) multiple clones are conducted or 3) duplicated regions undergo geometric transformations or signal corruptions. To overcome such limitations, we propose a fast and accurate copy-move forgery detection algorithm, based on complex-valued invariant features. First, dense and uniform keypoints are extracted from the whole image, even in small and smooth regions. Then, these keypoints are represented by robust and discriminative moment invariants, where a novel fast algorithm is designed especially for the computation of dense keypoint features. Next, an effective magnitude-phase hierarchical matching strategy is proposed for fast matching a massive number of keypoints while maintaining the accuracy. Finally, a reliable post-processing algorithm is developed, which can simultaneously reduce false negative rate and false positive rate. Extensive experimental results demonstrate the superior performance of our proposed scheme compared with existing state-of-the-art algorithms, with average pixel-level F-measure of 94.54% and average CPU-time of 36.25 seconds on four publicly available datasets.
Since digital images are one of the most important carriers of information, their authenticity is quite important. There are miscellaneous forgery techniques for manipulating digital images, and one of those is copy-move forgery. Many forgery detection techniques have been developed for detection of copy-move forgery so far. However, the main lack of these techniques is that although they can successfully detect the copied and pasted regions on a copy-move forgery image, they are not able to determine which of the detected regions is the source region and which of them is the destination region. In this study, a novel and standalone technique has been proposed for source-destination discrimination on copy-move forgery images. The proposed technique is based on machine learning and uses Support Vector Machine. Our technique can be regarded as an appendage for the classical copy-move forgery detection algorithms, which cannot make source-destination discrimination. To the best of our knowledge, the proposed technique is the first standalone technique which makes source-destination discrimination on copy-move forgeries, in the literature, and it is the only successful source-destination discrimination technique in the literature.
This paper proposes a fast hybrid image encryption method to address the cyber security problem of image transmission in networked inverted pendulum visual servo control systems (NIPVSCSs). Firstly, the original image is encrypted by ranks crossing to resist image content leakage. Secondly, the randomly spaced watermark embedding operation are interspersed in each round of ranks cross encryption to locate the tampered image region. Furthermore, by analyzing the extracted watermark, the attack intensity can be pre-judged, which are then used to determine whether to decrypt. The tamper detection ability of digital watermark technology is combined with the security and robustness of image encryption algorithm effectively. Finally, the experimental results and analysis confirm the performance of the proposed algorithm in precisely locating regional tampering whilst maintaining high security and efficiency.
The problem recent days, image processing, image falsification. Copy-transfer picture plays a critical role as editing techniques such as manipulating post-production, watermarking, etc. perform it. Efficient instruments are important for analyzing the detection of image forgery. In this proposal, we built this framework to detect forgery using the (LBP) method overlap block. Initially, Image is disintegrated into blocks that overlap using LBP, and (DWT) the order measure the usage of the lighting generation. Principle analysis of the part PQ is to balance each overlapping block between the chunks and the extraction of the element. Support Vector Machine (SVM) defines and decides the image, and calculates the standard image light deviation to detect the image of the forges. This study concluded that all kinds of image forgery be sorted using LBP to extract the image and DWT function introduced for compression of images. Also, it is possible to use the SVM classifier to slice the fake region and also to calculate the standard deviation of an image from the extraction of the feature. Such findings are significantly higher than recent image.
Image tampering detection is a well developed field that not only analyzes the authenticity of image but restores credibility of it. Motive of image tampering is to create false notion about an image in viewers prospective. Image can be forged by modifying various features of image or by adding or eliminating part of it. Copy-move tampering can be defined as the process of inserting or deleting image region from an image such that no proof of alteration is visible. In this paper we propose to use Neighbourhood Projection Embedding (NPE) with regard to detection of copy-move tampering. Neighbourhood information preserving property of NPE can effectively detect and localize tampering in the presence of various post-processing operations like, additive Gaussian noise, JPEG compression, brightness change, colour reduction and blur images effectively.
