Copy-move Forgery In Digital Images Research Articles

Performance Evaluation of Local Binary Patterns LBP for Copy-Move Forgery Detection in Digital Images: A Comparative Study

Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

A robust detection and localization technique for copy-move forgery in digital images

Image forensic analysis becomes a major role in the field of digital image security due to tampering and forgery. The image forgery violates the authenticity and ownership of digital images. Copy-move forgery considers a significant kind of image forensic analysis algorithm. In this kind, the forger copies a part of an original image and then pastes it into the selected position from the same image. The purpose of forgery is to hide or highlight a specific region of the original image. To detect copy-move forgery, there are two traditional techniques: block-based and keypoint-based. The main drawback of the keypoint-based technique is the insufficient features for the small and flat regions, which causes undetected forgery. In contrast, the block-based technique has intensive processing. Therefore, this paper proposes a robust scheme that overcomes the drawbacks of the above techniques and maintains their advantages. This scheme adopts three connected stages, the first detects the initial duplicated regions using the SURF-HOG detector and descriptor. Subsequently, the second stage localizes the primary matched regions by SLIC segmentation and then selects the suspicious neighbor regions to be combined with primary regions to obtain the active regions. In the third stage, the block-based technique adopts overlapping Zernike moments to extract sufficient key points from the produced active regions. In the final stage, the duplicated regions are classified into authentic or forged regions. The proposed scheme provides not only forgery detection but also localization and recognition for the duplicated regions. The experimental results show that the proposed scheme is fast and has high accuracy for forgery detection and localization, at least 93.75, and 7.25 in terms of True Positive and False Positive Rates. Moreover, the scheme has high robustness under various conditions and attacks such as geometric transformation attacks and compound photometric attacks. The proposed scheme can be used in sensitive applications such as cybercrime detection and adopted as evidence in the courts.

Detection and Localization of Copy-Move Forgery in Digital Images: Review and Challenges

Copy move forgery in digital images became a common problem due to the wide accessibility of image processing algorithms and open-source editing software. The human visual system cannot identify the traces of forgery in the tampered image. The proliferation of such digital images through the internet and social media is possible with a finger touch. These tampered images have been used in news reports, judicial forensics, medical records, and financial statements. In this paper, a detailed review has been carried on various copy-move forgery detection (CMFD) and localization techniques. Further, challenges in the research are identified along with possible solutions.

An intelligent block matching approach for localisation of copy-move forgery in digital images

Block-based copy-move forgery detection (CMFD) methods work with features from overlapping blocks. As overlapping blocks are involved, thresholds related to similarity and the physical distances are defined to identify the duplicated regions. However, these thresholds are controlled manually in localising the forged regions. In order to overcome this, an intelligent block matching approach for localisation is proposed using colour and texture features (CTF) through firefly algorithm. Investigation of the proposed CTF method is carried out on a standard database, which achieved an average true detection rate of 0.98 and an average false detection rate of 0.07. The proposed CTF method is robust against brightness change, colour reduction, blurring, contrast adjustment attacks, and additive white Gaussian noise. Performance analysis of the CTF method validates its superiority over other existing methods.

An intelligent block matching approach for localisation of copy-move forgery in digital images

Block-based copy-move forgery detection (CMFD) methods work with features from overlapping blocks. As overlapping blocks are involved, thresholds related to similarity and the physical distances are defined to identify the duplicated regions. However, these thresholds are controlled manually in localising the forged regions. In order to overcome this, an intelligent block matching approach for localisation is proposed using colour and texture features (CTF) through firefly algorithm. Investigation of the proposed CTF method is carried out on a standard database, which achieved an average true detection rate of 0.98 and an average false detection rate of 0.07. The proposed CTF method is robust against brightness change, colour reduction, blurring, contrast adjustment attacks, and additive white Gaussian noise. Performance analysis of the CTF method validates its superiority over other existing methods.

Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features

Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features

2-Levels of clustering strategy to detect and locate copy-move forgery in digital images

Understanding is considered a key purpose of image forensic science in order to find out if a digital image is authenticated or not. It can be a sensitive task in case images are used as necessary proof as an impact judgment. it’s known that There are several different manipulating attacks but, this copy move is considered as one of the most common and immediate one, in which a region is copied twice in order to give different information about the same scene, which can be considered as an issue of information integrity. The detection of this kind of manipulating has been recently handled using methods based on SIFT. SIFT characteristics are represented in the detection of image features and determining matched points. A clustering is a key step which always following SIFT matching in-order to classify similar matched points to clusters. The ability of the image forensic tool is represented in the assessment of the conversion that is applied between the two duplicated images of one region and located them correctly. Detecting copy-move forgery is not a new approach but using a new clustering approach which has been purposed by using the 2-level clustering strategy based on spatial and transformation domains and any previous information about the investigated image or the number of clusters need to be created is not necessary. Results from different data have been set, proving that the proposed method is able to individuate the altered areas, with high reliability and dealing with multiple cloning.

A Fast and Robust Approach to Detect Copy-Move Forgery in Digital Images

the present world, digital images and videos are our main source of information and these can be easily manipulated to conceal some meaningful information by using largely available powerful and sophisticated image editing tools. So in this era of illusions, verifying the authenticity of images and locating the tampering regions without using any prior knowledge is an important area of research. Copy-move forgery is one of the mostly used forgery technique. Many block matching algorithms are suggested to deal with this type of forgery but still there are some issues which are not properly addressed and need more attention such as time complexity. With increasing image size the execution time of detection algorithm is also increases. In this paper, we propose a method based on Discrete Cosine Transform (DCT) in order to improve time complexity. The proposed technique can also detect forgery even after some post processing operations such as rotation and Gaussian noise addition. Keywords-move forgery; Discrete Cosine Transform(DCT); Block matching; passive forgery detection; image forgery

On Image Forgery Detection Using Two Dimensional Discrete Cosine Transform and Statistical Moments

Abstract In the present communication, we propose a new approach for detecting copy-move forgery in digital images using statistical moments and two dimensional discrete cosine transform. We first slide a window centered around every pixel of the suspicious image, then each window is passed through two dimensional discrete cosine transform (2 D – DCT) to obtain the quantized coefficient matrix. The low dimensional statistical feature vector of each quantized coefficient matrix is obtained and arranged in a feature matrix F . The columns of F contain 4 - statistical features, i.e., mean Me , variance Var , third order moment skewness S k and fourth order moment kurtosis Kr obtained from the quantized coefficient matrix. In order to make similar windows adjacent, the feature matrix F is lexicographically sorted using radix sort. Finally, a copy-move forgery detection is performed using the adjacent pairs of feature vectors. It has also been observed that the proposed method has the lower dimension feature vector with lower computational complexity.

Blind Detection of Copy-Move Forgery in Digital Images Based on Dyadic Wavelet Transform

This paper proposed a detection algorithm for copy-move in same image based on dyadic wavelet transform. First of all, four sub images could be got through the decomposition of detecting image by dyadic wavelet transform. Secondly, high-frequency and low-frequency sub image were decomposed into blocks without any overlap and two sub image’s dyadic wavelet coefficients were regarded as the eigenvalue of the image block. At Last, both the high similarity among the low-frequency sub image blocks and the low similarity among the high-frequency sub image blocks were selected as a distorted image block. A kind of image edge processing methods was used to improve the tampering region at the same time. Through the experiments, it shows that the algorithm got the higher detection rate and lower error rates.

On the reliability of forensic schemes using resampling for image copy-move forgery

The goal of image forgery detection in multimedia security and forensics is to find a clue of image manipulation and prove the inauthenticity of digital images. The presence of the original and its duplicated regions in an image is regarded as a fingerprint for copy-move forgery. The problem investigated here concerns the situation when resampling process is employed in the pre-processing stage of detecting image copy-move forgery. In several detection techniques of copy-move forgery in digital images, resampling is utilised to lower the spatial resolution of an image, further improving the efficiency and the speed of image forensics. However, the reliability of detection methods has not been examined in detail. In this paper, we take a view of some recently-proposed forensic techniques using resampling and analyse the reliability of detecting copy-move forgery, by modelling copy-move forgery process and detection problem. Based on theoretical analysis and experimental validation it is concluded from this study that a correct detection of image copy-move tampering may be impeded by resampling operation under certain conditions and it is more difficult to reveal the forgery than previously thought.

A robust detection algorithm for copy-move forgery in digital images

With the availability of the powerful editing software and sophisticated digital cameras, region duplication is becoming more and more popular in image manipulation where part of an image is pasted to another location to conceal undesirable objects. Most existing techniques to detect such tampering are mainly at the cost of higher computational complexity. In this paper, we present an efficient and robust approach to detect such specific artifact. Firstly, the original image is divided into fixed-size blocks, and discrete cosine transform (DCT) is applied to each block, thus, the DCT coefficients represent each block. Secondly, each cosine transformed block is represented by a circle block and four features are extracted to reduce the dimension of each block. Finally, the feature vectors are lexicographically sorted, and duplicated image blocks will be matched by a preset threshold value. In order to make the algorithm more robust, some parameters are proposed to remove the wrong similar blocks. Experiment results show that our proposed scheme is not only robust to multiple copy-move forgery, but also to blurring or nosing adding and with low computational complexity.

Reduced Time Complexity for Detection of Copy-Move Forgery Using Discrete Wavelet Transform

As result of powerful image processing tools, digital image forgeries have already become a serious social problem. In this paper we describe an effective method to detect Copy-Move forgery in digital images. Our technique works by first applying DWT (Discrete Wavelet Transform) to the input image to yield a reduced dimensional representation [1]. Then the compressed image is divided into overlapping blocks. These blocks are then sorted and duplicated blocks are identified using Phase Correlation as similarity criterion. Due to DWT usage, detection is first carried out on lowest level image representation. This approach drastically reduces the time needed for the detection process and increases accuracy of detection process. General Terms Image processing, Digital image forgery