Copy-move Forgery Detection Algorithm Research Articles

Copy-move forgery detection algorithm based on binarized statistical image features and principal component analysis

Copy-move forgery detection algorithm based on binarized statistical image features and principal component analysis

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering

Image copy-move forgery is a common simple tampering technique. To address issues such as high time complexity in most copy-move forgery detection algorithms and difficulty detecting forgeries in smooth regions, this paper proposes an image copy-move forgery detection algorithm based on fused features and density clustering. Firstly, the algorithm combines two detection methods, speeded up robust features (SURF) and accelerated KAZE (A-KAZE), to extract descriptive features by setting a low contrast threshold. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm removes mismatched pairs and reduces false positives. To improve the accuracy of forgery localization, the algorithm uses the original image and the image transformed by the affine matrix to compare similarities in the same position in order to locate the forged region. The proposed method was tested on two datasets (Ardizzone and CoMoFoD). The experimental results show that the method effectively improved the accuracy of forgery detection in smooth regions, reduced computational complexity, and exhibited strong robustness against post-processing operations such as rotation, scaling, and noise addition.

A Novel Copy–Move Forgery Detection Algorithm via Gradient-Hash Matching and Simplified Cluster-Based Filtering

Copy–move forgery is one of the most frequently used methods for producing fake digital images. Current algorithms for copy–move forgery detection (CMFD) cannot combine high accuracy and fast speed. Motivated by the observation, we propose a novel CMFD algorithm whose workflow is as follows. First, we use a keypoint-extraction method with the lowest contrast threshold to extract more keypoints from the input image. Second, a new technique, gradient-hash matching, finds pairs of similar keypoints quickly and effectively using a hash table, where the hash value is computed using gradients of keypoints. Subsequently, a new method called simplified cluster-based filtering exploits the density pattern of keypoints in the copy–move regions to remove false matching keypoint pairs. Finally, image matting is applied to indicate the forgery regions vividly. Extensive experiments show that not only the new algorithm is better than the state-of-the-art algorithms in terms of computation correctness, but also its computation time is drastically less. Commonly only about half time is needed. The relative time saving is even higher when images are larger. Different algorithms modules are compared through experiments to choose the best combination.

A comparative analysis of Copy-move forgery detection algorithms

A comparative analysis of Copy-move forgery detection algorithms

A comparative analysis of copy-move forgery detection algorithms

A comparative analysis of copy-move forgery detection algorithms

A Novel Copy-Move Forgery Detection Algorithm via Feature Label Matching and Hierarchical Segmentation Filtering

Recently, some copy-move forgeries have made use of homogeneous region(s) in an image with the large-scaling attack(s) to highlight or cover the target objects, which is easy to implement but difficult to detect. Unfortunately, existing Copy-Move Forgery Detection (CMFD) methods fail to detect such kinds of forgeries because they are incapable of extracting a sufficient number of effective keypoints in the homogeneous region(s), leading to inaccuracy and inefficiency in detection. In this work, a new CMFD scheme is proposed: 1) An improved SIFT structure with inherent scaling invariance is designed to enhance the capability of extracting effective keypoints in the homogeneous region. 2). The enhancement of massive keypoints extraction in the homogeneous region incurs a heavy computational burden in feature matching (Note that this is a common issue in all CMFD methods). For this reason, a new Feature Label Matching (FLM) method is proposed to break down the massive keypoints into different small label groups, each of which contains only a small number of keypoints, for significantly improved matching effectiveness and efficiency. 3) Identifying true keypoints for matching is a critical issue for performance. In our work, the Hierarchical Segmentation Filtering (HSF) algorithm is newly proposed to filter out suspicious outliers, based on the statistics on the coarse-to-fine hierarchical segmentations. 4) Finally, the fusion of the coarse-to-fine hierarchical segmentation maps fills the forgery regions precisely. In our experiments, the proposed scheme achieves excellent detection performance under various attacks, especially for the homogeneous region(s) detection under large-scaling attack(s). Extensive experimental results demonstrate that the proposed scheme achieves the best F1 scores and least computational cost in addressing the geometrical attacks on the IMD dataset (a comprehensive dataset), and CMH datasets (most forgery samples under geometric attacks). Compared to existing state-of-the-art methods, the proposed scheme raises at least 20% and 25% in terms of F1 scores under scaling factors of 50%, and 200% in large-scaling sub-datasets of IMD, respectively.

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

<span>Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.</span>

A novel copy-move forgery detection algorithm via two-stage filtering

With the wide application of simple image editing software, forgery images have become severe social problems with extremely damaging effects. The copy-move forgery technique is commonly used to temper an image by copying some regions of an image and pasting them somewhere in the same image. In this paper, a novel algorithm is proposed for the detection of copy-move forgery. First, the keypoints of the input image are computed using a keypoint extraction algorithm. Second, a keypoint-matching algorithm is used to match similar keypoints as the candidate keypoint pairs. Third, a proposed novel Two-Stage Filtering algorithm, including the Grid-Based Filter and the Clustering-Based Filter, is applied to filter out most of the false matching keypoint pairs. Subsequently, image matting is achieved by the Delaunay triangulation algorithm so that the marked areas indicate the forgery regions. Three copy-move forgery datasets are used to compare the performances of the proposed algorithm with some state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm's overall performance is superior to other solutions for detecting copy-move forgery images.

Fast and effective Keypoint-based image copy-move forgery detection using complex-valued moment invariants

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 s on four publicly available datasets.

Source-destination discrimination on copy-move forgeries

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.

Copy-move forgery detection of digital images using football game optimization

ABSTRACT Copy move forgery (CMF) of digital images is the frequently adapted tampering by simply copying a region of the image and pasting it on to the same image using user-friendly image processing tools. Most of the existing key-point (KP) based CMF detection algorithms involve a hefty number of KPs and huge sets of feature descriptors, which increase their computational burden. In addition, the identified KPs may not spread over all regions of the image and the classical clustering techniques may not optimally classify the feature space into cluster space, thereby affecting the accuracy of the results. This paper suggests a new CMF detection method that considers a small number of the strongest KPs, chosen from both Difference of Gaussian (DoG)-based KPs and FAST-corner KPs, evaluates Scale Invariant Features Transform (SIFT) descriptors, applies discrete wavelet transform for dimensionality reduction, and employs football game based optimization (FGBO). The FGBO belongs to the family of meta-heuristic optimization algorithms, and is used for effectively classifying the feature space into cluster space, with a view to overcoming the drawbacks of classical methods. This article exhibits the superior performances of the developed method over existing methods by presenting results on 500 digital images.

A novel deep learning framework for copy-moveforgery detection in images

In this era of technology, digital images turn out to be ubiquitous in a contemporary society and they can be generated and manipulated by a wide variety of hardware and software technologies. Copy-move forgery is considered as an image tampering technique that aims to generate manipulated tampered images by concealing unwanted objects or reproducing desirable objects within the same image. Therefore, image content authentication has become an essential demand. In this paper, an innovative design for automatic detection of copy-move forgery based on deep learning approaches is proposed. A Convolutional Neural Network (CNN) is specifically designed for Copy-Move Forgery Detection (CMFD). The CNN is exploited to learn hierarchical feature representations from input images, which are used for detecting the tampered and original images. The extensive experiments demonstrate that the proposed deep CMFD algorithm outperforms the traditional CMFD systems by a considerable margin on the three publicly accessible datasets: MICC-F220, MICC-F2000, and MICC-F600. Furthermore, the three datasets are incorporated and joined to the SATs-130 dataset to form new combinations of datasets. An accuracy of 100% has been achieved for the four datasets. This proves the robustness of the proposed algorithm against a diversity of known attacks. For better evaluation, comparative results are included.

False Matches Removing in Copy-Move Forgery Detection Algorithms

Today the technology age is characterized by spreading of digital images. The most common form of transfer the information in magazines, newspapers, scientific journals and all types of social media. This huge use of images technology has been accompanied by an evolution in editing tools of image processing which make modifying and editing an image is very simple. Nowadays, the circulation of such forgery images, which distort the truth, has become common, intentionally or unintentionally. Nowadays many methods of copy-move forgery detection which is one of the most important and popular methods of image forgery are available. Most of these methods suffer from the problem of producing false matches as false positives in flat regions. This paper presents an algorithm of the Copy-Move forgery detection using the SIFT algorithm with an effective method to remove the false positives by rejecting all key-points in matches list that own a neighbor less than the threshold. The accuracy of the proposed algorithm was 95 %. The experimental results refer that the proposed method of false positives removing can remove false matches accurately and quickly.

Fast and Effective Image Copy-Move Forgery Detection via Hierarchical Feature Point Matching

Copy-move forgery is one of the most commonly used manipulations for tampering digital images. Keypoint-based detection methods have been reported to be very effective in revealing copy-move evidence due to their robustness against various attacks, such as large-scale geometric transformations. However, these methods fail to handle the cases when copy-move forgeries only involve small or smooth regions, where the number of keypoints is very limited. To tackle this challenge, we propose a fast and effective copy-move forgery detection algorithm through hierarchical feature point matching. We first show that it is possible to generate a sufficient number of keypoints that exist even in small or smooth regions by lowering the contrast threshold and rescaling the input image. We then develop a novel hierarchical matching strategy to solve the keypoint matching problems over a massive number of keypoints. To reduce the false alarm rate and accurately localize the tampered regions, we further propose a novel iterative localization technique by exploiting the robustness properties (including the dominant orientation and the scale information) and the color information of each keypoint. Extensive experimental results are provided to demonstrate the superior performance of our proposed scheme in terms of both efficiency and accuracy.

Two stages object recognition based copy-move forgery detection algorithm

Copy-Move Forgery Detection (CMFD) is a key issue of image forensics. A copy-move forgery is a type of image tampering that is created by copying a part of the image and pasting it on another part of the same image to perniciously hide or clone certain regions. This paper presents a new methodology for CMFD in digital images. The proposed algorithm is performed in two successive stages; matching stage and refinement stage. In the matching stage, close morphological operation and Connected Component Labeling (CCL) are used to segment the target image into different objects. The Speeded Up Robust Features (SURF) are extracted from each object and used to build an object catalog. The objects in the catalog are compared to each other, and matched objects are determined. If matched objects exist, the image is categorized as forged image. Otherwise, it is categorized as original image. The refinement stage, on the other hand, is implemented to ensure the originality of the target image. Thus, the candidate image that is classified as original is fed into the refinement stage to certify its originality. In this stage, close and open morphological operations as well as CCL are utilized to obtain the various objects in the image. Afterward, the SURF features are extracted from each object and used to build a new object catalog. The match between the objects in this catalog is obtained. If similar objects are found, the candidate image is classified as forged. Otherwise, the image is categorized as original. The proposed technique is assessed on four popular datasets. The results demonstrate the capability and robustness of the proposed technique in detecting the copy-move forgery under different geometrical attacks. Furthermore, the outcomes show that the suggested technique outperforms the previous CMFD methods in terms of Accuracy and execution time.

Fractional quaternion cosine transform and its application in color image copy-move forgery detection

In this paper, fractional quaternion cosine transforms (FrQCT) is proposed to generalize the conventional fractional cosine transforms (FrCT) to quaternion signal processing in a holistic manner. Firstly, the new transform FrQCT is defined and the proof of its inverse transform is presented. An efficient discrete implementation method of FrQCT is then proposed, in which the relationship between FrQCT and FrCT of four components is used for a quaternion signal. Finally, a new color image copy-move forgery detection algorithm based on FrQCT and modified PatchMatch matching algorithm is proposed to evaluate the performance of the proposed FrQCT. Experimental results on two public datasets (FAU dataset and GRIP dataset) demonstrate that: (a) the proposed efficient implementation method takes only half the computational time of the direct method; (b) the proposed FrQCT-based forgery detection algorithm can achieve a better performance than some state-of-the-art algorithms, especially in the additional operation case.

Evaluation of copy-move forgery detection: datasets and evaluation metrics

Creating copy-move forgery became even easier using a wide range of software and platforms. Many algorithms have been proposed to solve the problem, but each one of those algorithms has its own drawbacks. Researchers face many challenges in developing copy-move detection algorithms, and in this paper, we focus on two challenges. The first is the benchmark dataset, and the second involves evaluation metrics. In this paper, we investigate the available copy-move datasets and their advantages and disadvantages. In addition, we discuss the different metrics that have been used by researchers to evaluate the copy-move forgery detection (CMFD) algorithms. On that basis, we suggest the standard specifications of the appropriate copy-move dataset and the metrics that should be used to evaluate the detection algorithms. The findings of this paper will help researchers evaluate their algorithms effectively and fairly essential for developing reliable algorithms.

Fast copy-move forgery detection using local bidirectional coherency error refinement

In this paper, we present an algorithm that can accurately and robustly detect regions of copy-move forgery. We firstly adapt and enhance a coherency sensitive hashing method to establish the feature correspondences in an image. Then, a local bidirectional coherency error is proposed to refine the feature correspondences via iteration over the enhanced coherency sensitive search. When the variation in the local bidirectional coherency error of the host image is not larger than a specified threshold, the iterative process stops, indicating that the feature correspondences are stable. In the end, from the stable feature correspondences, the copy-move forgery regions are easily detected using the local bidirectional coherency error of each feature. The experimental results show the proposed detection method achieves real-time or near real-time effectiveness; at the same time, it can achieve very good detection results compared with the state-of-the-art copy-move forgery detection algorithms, even under various challenging conditions.

Nonoverlapping Blocks Based Copy-Move Forgery Detection

In order to solve the problem of high computational complexity in block-based methods for copy-move forgery detection, we divide image into texture part and smooth part to deal with them separately. Keypoints are extracted and matched in texture regions. Instead of using all the overlapping blocks, we use nonoverlapping blocks as candidates in smooth regions. Clustering blocks with similar color into a group can be regarded as a preprocessing operation. To avoid mismatching due to misalignment, we update candidate blocks by registration before projecting them into hash space. In this way, we can reduce computational complexity and improve the accuracy of matching at the same time. Experimental results show that the proposed method achieves better performance via comparing with the state-of-the-art copy-move forgery detection algorithms and exhibits robustness against JPEG compression, rotation, and scaling.