Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality a...Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality and quantity,and a narrow range of applicable tasks.These limitations significantly restrict the capacity and applicability of CMFD.To overcome the limitations of existing methods,a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach.Firstly,this study formulates the objective task and network relationship as an optimization problem using transfer learning.Furthermore,it thoroughly discusses and analyzes the relationship between CMFD and deep network architecture by employing ResNet-50 during the optimization solving phase.Secondly,a quantitative comparison between fine-tuning and feature decoupling is conducted to evaluate the degree of similarity between the image classification and CMFD domains by the enhanced ResNet-50.Finally,suspicious regions are localized using a feature pyramid network with bottom-up path augmentation.Experimental results demonstrate that IMTNet achieves faster convergence,shorter training times,and favorable generalization performance compared to existingmethods.Moreover,it is shown that IMTNet significantly outperforms fine-tuning based approaches in terms of accuracy and F_(1).展开更多
Most existing methods for image copy-move forgery detection(CMFD)operate on grayscale images. Although the keypoint-based methods have the advantages of strong robustness and low computational cost,they cannot identif...Most existing methods for image copy-move forgery detection(CMFD)operate on grayscale images. Although the keypoint-based methods have the advantages of strong robustness and low computational cost,they cannot identify the flat duplicated regions without reliable extracted features. In this paper, we propose a new CMFD method by using speeded-up robust feature(SURF)in the opponent color space. Our method starts by converting the inspected image from RGB to the opponent color space. The color gradient per pixel is calculated and taken as the work space for SURF to extract the keypoints. The matched keypoints are clustered and their geometric transformations are estimated. Finally, the false matches are removed. Experimental results show that the proposed technique can effectively expose the duplicated regions with various transformations, even when the duplication regions are flat.展开更多
An effective algorithm is proposed to detect copy-move forgery.In this algorithm,first,the PatchMatch algorithm is improved by using a reliable order-statistics-based approximate nearest neighbor search algorithm(ROSA...An effective algorithm is proposed to detect copy-move forgery.In this algorithm,first,the PatchMatch algorithm is improved by using a reliable order-statistics-based approximate nearest neighbor search algorithm(ROSANNA)to modify the propagation process.Then,fractional quaternion Zernike moments(FrQZMs)are considered to be features extracted from color forged images.Finally,the extracted FrQZMs features are matched by the improved PatchMatch algorithm.The experimental results on two publicly available datasets(FAU and GRIP datasets)show that the proposed algorithm performs better than the state-of-the-art algorithms not only in objective criteria F-measure value but also in visual.Moreover,the proposed algorithm is robust to some attacks,such as additive white Gaussian noise,JPEG compression,rotation,and scaling.展开更多
Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approach...Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.展开更多
Digital images can be tampered easily with simple image editing software tools.Therefore,image forensic investigation on the authenticity of digital images’content is increasingly important.Copy-move is one of the mo...Digital images can be tampered easily with simple image editing software tools.Therefore,image forensic investigation on the authenticity of digital images’content is increasingly important.Copy-move is one of the most common types of image forgeries.Thus,an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper.These methods are classified into three types:block-based methods,keypoint-based methods,and deep learning-based methods.In addition,the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost.Finally,further research directions are discussed.展开更多
Image forgery detection remains a challenging problem.For the most common copy-move forgery detection,the robustness and accuracy of existing methods can still be further improved.To the best of our knowledge,we are t...Image forgery detection remains a challenging problem.For the most common copy-move forgery detection,the robustness and accuracy of existing methods can still be further improved.To the best of our knowledge,we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network(PCNN)and the self-selected sub-images.Our method has the following steps:First,contour detection is performed on the input color image,and bounding boxes are drawn to frame the contours to form suspected forgery sub-images.Second,by improving PCNN to perform feature extraction of sub-images,the feature invariance of rotation,scaling,noise adding,and so on can be achieved.Finally,the dual feature matching is used to match the features and locate the forgery regions.What’s more,the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction,and the improved PCNN can extract image features with high robustness.Through experiments on the standard image forgery datasets CoMoFoD and CASIA,it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method,which is a more efficient image copy-move forgery passive detection method.展开更多
Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain a...Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images.Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries.However,an image can contain one or more types of forgeries.This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain.Firstly,images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing forgery.The proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and healthy.Benchmarked(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and testing.On average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.展开更多
基金supported and founded by the Guizhou Provincial Science and Technology Project under the Grant No.QKH-Basic-ZK[2021]YB311the Youth Science and Technology Talent Growth Project of Guizhou Provincial Education Department under Grant No.QJH-KY-ZK[2021]132+2 种基金the Guizhou Provincial Science and Technology Project under the Grant No.QKH-Basic-ZK[2021]YB319the National Natural Science Foundation of China(NSFC)under Grant 61902085the Key Laboratory Program of Blockchain and Fintech of Department of Education of Guizhou Province(2023-014).
文摘Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality and quantity,and a narrow range of applicable tasks.These limitations significantly restrict the capacity and applicability of CMFD.To overcome the limitations of existing methods,a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach.Firstly,this study formulates the objective task and network relationship as an optimization problem using transfer learning.Furthermore,it thoroughly discusses and analyzes the relationship between CMFD and deep network architecture by employing ResNet-50 during the optimization solving phase.Secondly,a quantitative comparison between fine-tuning and feature decoupling is conducted to evaluate the degree of similarity between the image classification and CMFD domains by the enhanced ResNet-50.Finally,suspicious regions are localized using a feature pyramid network with bottom-up path augmentation.Experimental results demonstrate that IMTNet achieves faster convergence,shorter training times,and favorable generalization performance compared to existingmethods.Moreover,it is shown that IMTNet significantly outperforms fine-tuning based approaches in terms of accuracy and F_(1).
基金Supported by the Natural Science Foundation of Tianjin(No.15JCYBJC15500)
文摘Most existing methods for image copy-move forgery detection(CMFD)operate on grayscale images. Although the keypoint-based methods have the advantages of strong robustness and low computational cost,they cannot identify the flat duplicated regions without reliable extracted features. In this paper, we propose a new CMFD method by using speeded-up robust feature(SURF)in the opponent color space. Our method starts by converting the inspected image from RGB to the opponent color space. The color gradient per pixel is calculated and taken as the work space for SURF to extract the keypoints. The matched keypoints are clustered and their geometric transformations are estimated. Finally, the false matches are removed. Experimental results show that the proposed technique can effectively expose the duplicated regions with various transformations, even when the duplication regions are flat.
基金The National Natural Science of China(No.61572258,61771231,61772281,61672294)the Priority Academic Program Development of Jiangsu Higher Education Institutionsthe Qing Lan Project of Jiangsu Higher Education Institutions
文摘An effective algorithm is proposed to detect copy-move forgery.In this algorithm,first,the PatchMatch algorithm is improved by using a reliable order-statistics-based approximate nearest neighbor search algorithm(ROSANNA)to modify the propagation process.Then,fractional quaternion Zernike moments(FrQZMs)are considered to be features extracted from color forged images.Finally,the extracted FrQZMs features are matched by the improved PatchMatch algorithm.The experimental results on two publicly available datasets(FAU and GRIP datasets)show that the proposed algorithm performs better than the state-of-the-art algorithms not only in objective criteria F-measure value but also in visual.Moreover,the proposed algorithm is robust to some attacks,such as additive white Gaussian noise,JPEG compression,rotation,and scaling.
基金supported in part by the National Natural Science Foundation of China under grant No.(61472429,61070192,91018008,61303074,61170240)Beijing Natural Science Foundation under grant No.4122041+1 种基金National High-Tech Research Development Program of China under grant No.2007AA01Z414National Science and Technology Major Project of China under grant No.2012ZX01039-004
文摘Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.
文摘Digital images can be tampered easily with simple image editing software tools.Therefore,image forensic investigation on the authenticity of digital images’content is increasingly important.Copy-move is one of the most common types of image forgeries.Thus,an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper.These methods are classified into three types:block-based methods,keypoint-based methods,and deep learning-based methods.In addition,the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost.Finally,further research directions are discussed.
基金supported by National Natural Science Foundation of China(Grants Nos 61772327,61532021)Project of Electric Power Research Institute of State Grid Gansu Electric Power Company(H2019-275).
文摘Image forgery detection remains a challenging problem.For the most common copy-move forgery detection,the robustness and accuracy of existing methods can still be further improved.To the best of our knowledge,we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network(PCNN)and the self-selected sub-images.Our method has the following steps:First,contour detection is performed on the input color image,and bounding boxes are drawn to frame the contours to form suspected forgery sub-images.Second,by improving PCNN to perform feature extraction of sub-images,the feature invariance of rotation,scaling,noise adding,and so on can be achieved.Finally,the dual feature matching is used to match the features and locate the forgery regions.What’s more,the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction,and the improved PCNN can extract image features with high robustness.Through experiments on the standard image forgery datasets CoMoFoD and CASIA,it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method,which is a more efficient image copy-move forgery passive detection method.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R236),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images.Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries.However,an image can contain one or more types of forgeries.This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain.Firstly,images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing forgery.The proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and healthy.Benchmarked(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and testing.On average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.
