Copy-move Image Forgery Research Articles

Retracted: Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection.

[This retracts the article DOI: 10.1155/2022/8501738.].

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.

ERINet: efficient and robust identification network for image copy-move forgery detection and localization

ERINet: efficient and robust identification network for image copy-move forgery detection and localization

Image copy-move forgery detection based on dynamic threshold with dense points

Copy-move tampering is one of the most popular tampering techniques at present. The tampered region of the image has good fusion with the original image, which increases the difficulty of detection. After years of research, the current detection method based on key points still has the following problems: 1) Failure to achieve forgery detection of small areas/self-similar areas/smooth areas, 2) Lack of reasonable feature point extraction methods, 3) The various stages of copy-move forgery detection (CMFD) work are relatively independent and lack close connections, 4) A fixed threshold is used as the region of interest similarity metric in the matching and localization stages. The failure to consider tampered images and the diversity of tampered regions leads to the limited detection capability of the algorithm. Considering the actual situation of tampering with the picture, to solve the above problems, we propose a copy-move forgery detection method based on the dynamic threshold. First, we determine the point extraction strategy in each super pixel block according to the size of the simple line interface calculation (SLIC) super pixel block and the Weber local descriptor (WLD) descriptor to ensure the reasonable allocation of feature points and reduce unnecessary points. These key points are then characterized by the scaling, flip and rotation invariants of the fractional general Jacobi-Fourier moments (FJFMs). Then, the matching and mismatch filtering thresholds of each feature point are determined through the WLD and SLIC features, and the SLIC feature is used to replace the distance threshold to improve the detection accuracy of small manufacturing areas. Finally, based on the matching results and SLIC features, an effective positioning method is proposed to improve the speed and accuracy of positioning. Experimental results show that the proposed algorithm is superior to the classic methods in recent years in terms of time and accuracy.

QDL-CMFD: A Quality-independent and deep Learning-based Copy-Move image forgery detection method

One of the prevalent methods of image forgery is copy-move, where one or more regions of an image are duplicated and moved elsewhere in the image. It is usually difficult to detect this type of forgery due to the similarity of the copied and forged areas. Also, forgers perform pre-processing and/or post-processing operations on the manipulated regions to make it even more difficult to detect. In this study, an image quality-independent method based on deep learning approach, termed QDL-CMFD, is presented for detecting this type of forgery. QDL-CMFD utilizes generative adversarial networks for image quality enhancement, and convolutional neural networks (CNN) for forgery detection. A tailored dual-branch CNN architecture is introduced consisting of two subnetworks, namely a manipulation detection subnetwork and a similarity detection subnetwork. Accordingly, unlike most existing methods, QDL-CMFD is able to simultaneous detection of several forged areas, as well as determining the source and target of the forgery. Also, QDL-CMFD is robust against various pre-processing/post-processing attacks. It shows excellent performance for detecting low-quality forged images and small areas. Experiments conducted on the CASIA and CoMoFoD benchmark datasets confirm that QDL-CMFD performs significantly better than the competitors. All the implementation source codes of QDL-CMFD are available at https://github.com/MehradAria/QDL-CMFD.

Image copy-move forgery detection and localization based on super-BPD segmentation and DCNN

With the increasing importance of image information, image forgery seriously threatens the security of image content. Copy-move forgery detection (CMFD) is a greater challenge because its abnormality is smaller than other forgeries. To solve the problem that the detection results of the most image CMFD based on convolutional neural networks (CNN) have relatively low accuracy, an image copy-move forgery detection and localization based on super boundary-to-pixel direction (super-BPD) segmentation and deep CNN (DCNN) is proposed: SD-Net. Firstly, the segmentation technology is used to enhance the connection between the same or similar image blocks, improving the detection accuracy. Secondly, DCNN is used to extract image features, replacing conventional hand-crafted features with automatic learning features. The feature pyramid is used to improve the robustness to the scaling attack. Thirdly, the image BPD information is used to optimize the edges of rough detected image and obtain final detected image. The experiments proved that the SD-Net could detect and locate multiple, rotated, and scaling forgery well, especially large-level scaling forgery. Compared with other methods, the SD-Net is more accurately located and robust to various post-processing operations: brightness change, contrast adjustments, color reduction, image blurring, JPEG compression, and noise adding.

Coarse-to-fine spatial-channel-boundary attention network for image copy-move forgery detection

Coarse-to-fine spatial-channel-boundary attention network for image copy-move forgery detection

An efficient model for copy-move image forgery detection

PurposeThis study aims to solve problems of detecting copy-move images. With input images, the problem aims to: Confirm the original or forgery of the images, evaluate the performance of the detection and compare the proposed method’s effectiveness to the related ones.Design/methodology/approachThis paper proposes an algorithm to identify copy-move images by matching the characteristics of objects in the same group. The method is carried out through two stages of grouping the objects and comparing objects’ features. The classification and clustering can improve processing time by skipping groups of only one object, and feature comparison on objects in the same group improves accuracy of the detection. YOLO5, the latest version of you only look once (YOLO) developed by Ultralytics LLC, and K-means are applied to classify and group the objects in the first stage. Then, modified Zernike moments (MZMs) and correlation coefficients are used for the features extraction and matching in the second stage. The Open Images V6 data set is used to train the YOLO5 model. The combination of YOLO5 and MZM makes the effectiveness of the proposed method for copy-move image detection with an average accuracy of 94.26% for images of benchmark and MICC-F600 and 95.37% for natural images. The outstanding feature of the method is that it can balance both processing time and accuracy in detecting duplicate regions on the image.FindingsThe problem is then solved by doing the following steps: Build a method to detect objects and compare their features to find the similarity if they are copy-move objects; use YOLO5 for the object detection and group the same category objects; ignore the group having only one object and extract the features of the other groups by MZMs; detect copy-move regions using K-means clustering; and calculate and compare the detection accuracy of the proposed method and related methods.Originality/valueThe main contributions of this paper include: Reduce the processing time by using YOLO5 in objects detection and K-means in clustering; improve the accuracy by using MZM to extract features and correlation coefficients to matching them; and implement and prove the effectiveness of the proposed method for three copy-move data sets: benchmark, MICC-F600 and author-built images.

FAST, BRIEF and SIFT based image copy-move forgery detection technique

FAST, BRIEF and SIFT based image copy-move forgery detection technique

Copy-move image forgery detection based on evolving circular domains coverage

The aim of this paper is to improve the accuracy of copy-move forgery detection (CMFD) in image forensics by proposing a novel scheme and the main contribution is evolving circular domains coverage (ECDC) algorithm. The proposed scheme integrates both block-based and keypoint-based forgery detection methods. Firstly, the speed-up robust feature (SURF) in log-polar space and the scale invariant feature transform (SIFT) are extracted from an entire image. Secondly, generalized 2 nearest neighbor (g2NN) is employed to get massive matched pairs. Then, random sample consensus (RANSAC) algorithm is employed to filter out mismatched pairs, thus allowing rough localization of counterfeit areas. To present these forgery areas more accurately, we propose the efficient and accurate ECDC algorithm to present them. This algorithm can find satisfactory threshold areas by extracting block features from jointly evolving circular domains, which are centered on matched pairs. Finally, morphological operation is applied to refine the detected forgery areas. Experimental results indicate that the proposed CMFD scheme can achieve better detection performance under various attacks compared with other state-of-the-art CMFD schemes.

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.

Seamless Copy–Move Replication in Digital Images

The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy–move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy–move forgery.

An Implementation of Copy-Move Forgery Detector using SIFT-based Feature Refinement

This paper deals with refining the SIFT-based feature set for the Copy-Move forgery detection in image forensics. Many researchers attempt to extract the feature with advanced performance; however, high-dimensional feature sets increment the calculation cost and the detection time. For this reason, we intend to design a simple feature yet have good copy-move forgery detection performance. In addition to that, for various conditions of images, we extract the refined SIFT-based features of robust yet straightforward features applied to Copy-Move image forensics using only the SIFT features that have excellent invariance maintenance. In a Copy-Move forgery image, a definition has a similar distance and slope between a copy and move area. This paper proposed a new scheme for a Copy-Move forgery detection to satisfy the purpose. As a result, the Copy-Move area is detected with 97.8% upper. Thus, the detection ratio is confirmed as an

Gaussian Pyramid Decomposition in Copy-Move Image Forgery Detection with SIFT and Zernike Moment Algorithms

One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in plain copy-move, copy-move with rotation transformation, copy-move with JPEG compression, multiple copy-move, copy-move with reflection attack, and copy-move with image inpainting. We also examine the detections result with different values of gaussian pyramid limit and different area separation ratios. In detection with plain copy-move images, it generates low level of accuracy, precision and recall of 58.46%, 18.21% and 69.39%, respectively. The results are getting worse in for copy-move detection with reflection attack and copy-move with image inpainting. This weakness happened because this method has not been able to detect the position of the part of the image that is considered symmetrical and check whether the forged part uses samples from other parts of the image.

An efficient approach for copy-move image forgery detection using convolution neural network

An efficient approach for copy-move image forgery detection using convolution neural network

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection.

Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model's weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks' outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model's performance. The experimental results established the proposed model's superiority over recently developed approaches.

An Efficient CNN Model to Detect Copy-Move Image Forgery

Recently, digital images have become used in many applications, where they have become the focus of digital image processing researchers. Image forgery represents one hot topic on which researchers prioritize their studies. We concentrate on the copy-move image forgery topic as a deceptive forgery type. In copy-move image forgery, a part of an image is copied and placed in the same image to produce the forgery image. This paper proposes an accurate convolutional neural network(CNN) architecture for the effective detection of copy-move image forgery. The proposed architecture is computationally lightweight with a suitable number of convolutional and max-pooling layers. We also present a fast and accurate testing process with 0.83 seconds for every test. Many empirical experiments have been conducted to ensure the efficiency of the proposed model in terms of accuracy and time. These experiments were done on benchmark datasets and have achieved 100% accuracy.

Comparison of Edge Detection Algorithms for Texture Analysis on Copy-Move Forgery Detection Images

Feature extraction in Copy-Move Forgery Detection (CMFD) is crucial to facilitate image forgery analysis. Edge detection is one of the processes to extract specific information from Copy-Move Forgery (CMF) Images. It sensitizes the amount of information in the image and filters out useless ones while preserving the important structural properties in the image. This paper compares five edge detection methods: Robert, Sobel, Prewitt (first Derivative), Laplacian, and Canny edge detectors (second Derivatives). CMFD evaluation datasets images (MICC-F220) are tested with both methods to facilitate comparison. The edge detection operators were implemented with their respective convolution masks. Robert with a 2x2 mask, The Prewitt and Sobel with a 3x3 mask, while Laplacian and canny used adjustable masks. These masks determine the quality of the detected edges. Edges reflect a great-intensity contrast that is either darker or brighter.

Copy-move image forgery detection using cuckoo search

Most people face the dilemma of accepting photographs as authentic or not, mainly in the case of forensics, where the images will influence judgment. Research communities are constantly providing methods to identify these kinds of forged images. Attention capture is specifically the cases where a region of the image is copied in the same image (copy-move forgery - CMF). To detect these forged images, a recent approach named scale-invariant features transform (SIFT) has proved its worth and robustness in various geometrical transformations. However, the framework needs to be optimised for numerous parameters involved because a wrong selection of values leads to wrong identifications. To solve this problem, a novel method has been proposed named as cuckoo search-based copy-move forgery detection (CSCMFD) for optimising the parameter values in the SIFT framework. The CSCMFD demonstrates to attain better results by automatically determining the values of different parameters as compared to state of art research work. Experimentation is performed on the Christlein et al. database and MICC-F220 dataset. The experimental results proved that CSCMFD is able to capture small-forged areas as well as regions that are difficult to identify by other methods.

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images