Image Forgery Detection Using Noise and Edge Weighted Local Texture Features (original) (raw)
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Detection of Digital Image Forgery using Fast Fourier Transform and Local Features
2019 International Conference on Automation, Computational and Technology Management (ICACTM), 2019
Multimedia security is one of the key challenges in today's world, as dependency on multimedia information is increasing day by day. Easily available image editing software have enabled every common user of a smart phone and computer, to hack into the information of the images and video and alter it to some extent. To authenticate the genuineness of images, detection of image tempering is need of the time. Various techniques have been proposed to use image features for detection of image forgery. The techniques of forgery detection work in two domains of image forgery; copy-move forgery detection (CMFD) and image splicing detection (ISD). This paper presents a comprehensive comparative analysis for the use of local texture descriptors i.e. local binary pattern (LBP) and local ternary pattern (LTP) for forgery detection in an image. The paper also presents a technique to integrate fast fourier transform (FFT) with local texture descriptors for image forgery detection using existing block-based methodology. Performance of the technique(s) and descriptor(s) is tested for benchmarked dataset CASIA v1.0. Results are evaluated by using standard detection metrics detection accuracy and recall. The paper also suggests a relatively better texture descriptor.
Comparison between WLD and LBP descriptors for non-intrusive image forgery detection
2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014
Due to the availability of easy-to-use and powerful image editing tools, the authentication of digital images cannot be taken for granted and it gives rise to non-intrusive forgery detection problem because all imaging devices do not embed watermark. We investigated the detection of copy-move and splicing, the two harmful types of image forgery, using textural properties of images. Tampering distorts the texture micropatterns in an image and texture descriptors can be employed to detect tampering. We did comparative study to examine the effect of two state-of-the-art best texture descriptors: Multiscale Local Binary Pattern (Multi-LBP) and Multiscale Weber Law Descriptor (Multi-WLD). Multiscale texture descriptors extracted from the chrominance components of an image are passed to Support Vector Machine (SVM) to identify it as authentic or forged. The performance comparison reveals that Multi-WLD performs better than Multi-LBP in detecting copymove and splicing forgeries. Multi-WLD also outperforms stateof-the-art passive forgery detection techniques.
A generic passive image forgery detection scheme using local binary pattern with rich models
Computers & Electrical Engineering, 2017
Image forgery detection is one of the prominent areas from research and development perspective. This research work aims to propose a scheme for the detection of multiple types of image forgeries. In this paper, a generic passive image forgery scheme is proposed using spatial rich model (SRM) in combination with textural feature i.e. local binary pattern (LBP). Moreover, different sub-model selection strategies are implemented and analyzed to investigate the performance-to-model dimensionality trade-off. Ensemble multi-class classifier is used for classifying the features into different forgery classes. The proposed scheme is evaluated on the dataset generated from IEEE IFS-TC image forensics challenge containing 10 different kinds of forgeries. The results reveal that computing LBP on noise residuals in conjunction with co-occurrence matrices using BEST-q-CLASS feature selection strategy produces a model which performs efficiently for almost any set of modifications with accuracy of 98.4%.
In this paper, we introduce a new view of Local Binary Pattern (LBP) fit with SIFT (Scale Invariant Features Transform) for robust copy-move forgery detection. This method works by computing rotation invariant subuniform local binary patterns from an image keypoints. They have performed well rotation invariance by moving into the first column the dominant bins of subuniform pattern, and circularly shifted the others bins. The image is first converted into a grayscale image, then we apply SIFT algorithm to detect scale invariant key points from the image. Subsequently, we compute the subuniform LBP and extracting the feature vector, which are matched by using Chisquare distance. Furthermore we adopt the RANSAC (Random Sample Consensus) algorithm to remove mismatches. Our experimental results reveal that the proposed method can produce accurate detection results, and it exhibits high robustness to scale and rotate forged regions.
Edge-texture feature-based image forgery detection with cross-dataset evaluation
2019
A digital image is a rich medium of information. The development of user-friendly image editing tools has given rise to the need for image forensics. The existing methods for the investigation of the authenticity of an image perform well on a limited set of images or certain datasets but do not generalize well across different datasets. The challenge of image forensics is to detect the traces of tampering which distorts the texture patterns. A method for image forensics is proposed, which employs discriminative robust local binary patterns for encoding tampering traces and a support vector machine for decision making. In addition, to validate the generalization of the proposed method, a new dataset is developed that consists of historic images, which have been tampered with by professionals. Extensive experiments were conducted using the developed dataset as well as the public domain benchmark datasets; the results demonstrate the robustness and effectiveness of the proposed method for tamper detection and validate its cross-dataset generalization. Based on the experimental results, directions are suggested that can improve dataset collection as well as algorithm evaluation protocols. More broadly, discussion in the community is stimulated regarding the very important, but largely neglected, issue of the capability of image forgery detection algorithms to generalize to new test data.
Passive detection of image forgery using DCT and local binary pattern
Signal, Image and Video Processing, 2016
With the development of easy-to-use and sophisticated image editing software, the alteration of the contents of digital images has become very easy to do and hard to detect. A digital image is a very rich source of information and can capture any event perfectly, but because of this reason, its authenticity is questionable. In this paper, a novel passive image forgery detection method is proposed based on Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) to detect copy-move and splicing forgeries. First, from the chrominance component of the input image, discriminative localized features are extracted by applying 2D DCT in LBP space. Then, support vector machine (SVM) is used for detection. Experiments carried out on three image forgery benchmark datasets demonstrate the superiority of the method over recent methods in terms of detection accuracy.
Detection of Image Forgery using LBP and DCT Techniques
A digital image plays a very crucial role for an insurance claim, as illustrative information for a news item and as evidence in judiciary system. Nevertheless, the development of effective image editing tools that effortlessly change the image contents without leaving any visible indications of such alterations makes the genuineness of the digital image suffer from dangerous threats. This has led to demonstration and proposal of various methods to check that the digital images are genuine. To detect the digital image forgery, active methods require pre-embedding of a digital signature or watermark. Generally, all digital cameras can embed such watermark or signature and thus the need of passive methods that depend completely on the features of the digital image were required. There are various passive techniques that exist and meet these difficulties, but there are no satisfactory solutions so far. This paper proposes a passive technique for Image Forgery Detection system that is designed to detect the most common types of forgery like, splicing and copy-move. Image splicing is most common type of forgery, in which forgery is carried out through copying a small part from one base image and pasting to some other image. Whereas in copy-move forgery, copied part is pasted somewhere else in the same base image to either hide or add objects. The proposed system in this work is established on Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT). Firstly, the chrominance component of original input image is divided up into overlapping blocks. After that, for each block, Local Binary Pattern (LBP) is computed and modified into frequency domain using 2 Dimensional Discrete Cosine Transform (DCT). At last, Standard Deviations are computed for frequency coefficients of all blocks respectively and hence used as the features. A Support Vector Machine (SVM) is utilized for classification. Experimental results of different benchmark image forgery databases demonstrate that the detection accuracy of proposed technique in this work is up to 89%. MATLAB R2014b tool is used to implement the proposed system.
Image Forgery Detection by using Machine Learning
International Journal of Innovative Technology and Exploring Engineering, 2019
Dense local descriptors and AI havebeen utilized with achievement in a couple of employments, as classificationof surfaces, steganalysis, and bowing zone. We build up a newimage counterfeit marker creating unequivocal descriptors recentlyproposed in the steganalysis field reasonably joining some of suchdescriptors, and redesigning a SVM classifier on the availabletraining set. The issue with the present making is that majorityof them see certain highlights in pictures changed by a particular tamperingmethod, (for example, duplicate move, joining, and so forth). This proposes the structure does notwork always transversely over different evolving frameworks. Mix of no under two pictures to make a completely phony picture is known as Image structure. It winds up being difficult to disengage between certified picture and phony picture in light of the closeness of different astounding changing programming endeavors. In this paper, we propose a two phase imperative altering way to deal wi...
Forgery detection algorithm based on texture features
Indonesian Journal of Electrical Engineering and Computer Science
Any researcher's goal is to improve detection accuracy with a limited feature vector dimension. Therefore, in this paper, we attempt to find and discover the best types of texture features and classifiers that are appropriate for the coarse mesh finite differenc (CMFD). Segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and Haralick are the texture features that have been chosen. K-nearest neighbors (KNN), naïve Bayes, and Logistics are also among the classifiers chosen. SFTA, local binary pattern (LBP), and Haralick feature vector are fed to the KNN, naïve Bayes, and logistics classifier. The outcomes of the experiment indicate that the SFTA texture feature surpassed all other texture features in all classifiers, making it the best texture feature to use in forgery detection. Haralick feature has the second-best texture feature performance in all of the classifiers. The performance using the LBP feature is lower than that of the other texture featur...
IRJET- Image Forgery Detection using Local Binary Patterns
IRJET, 2020
The paper proposes Image forgery detection using Local Binary Patterns. Local Binary Pattern (LBP) is basically used for feature extraction. A simple LBP operator is calculated in a rectangular window. The main advantage of this original LBP implementation is that we can gain extremely small details in the image. However, the biggest downside of this algorithm is that we cannot capture fine details at varying scales, only the fixed 3 x 3 scale. To handle this drawback of variable neighbourhood sizes we use an extension to the original LBP implementation, it has varying p and r which are used to construct Local Binary Patterns where p is the number of points 'p' in a circularly symmetric neighbourhood and r is the radius of the circle 'r', which allows us to have values at different scales after the LBP features are obtained. We generate a normalized Histogram which is called as LBP histogram and for the classification purpose linear SVM classifier is used which will determine whether the input image is authentic or forged.