In existing image manipulation localization methods,the receptive field of standard convolution is limited,and during feature transfer,it is easy to lose high-frequency information about traces of manipulation.In addi...In existing image manipulation localization methods,the receptive field of standard convolution is limited,and during feature transfer,it is easy to lose high-frequency information about traces of manipulation.In addition,during feature fusion,the use of fixed sampling kernels makes it difficult to focus on local changes in features,leading to limited localization accuracy.This paper proposes an image manipulation localization method based on dual-branch hybrid convolution.First,a dual-branch hybrid convolution module is designed to expand the receptive field of the model to enhance the feature extraction ability of contextual semantic information,while also enabling the model to focus more on the high-frequency detail features of manipulation traces while localizing the manipulated area.Second,a multiscale content-aware feature fusion module is used to dynamically generate adaptive sampling kernels for each position in the feature map,enabling the model to focus more on the details of local features while locating the manipulated area.Experimental results on multiple datasets show that this method not only effectively improves the accuracy of image manipulation localization but also enhances the robustness of the model.展开更多
Existing image manipulation localization(IML)techniques require large,densely annotated sets of forged images.This requirement greatly increases labeling costs and limits a model’s ability to handle manipulation type...Existing image manipulation localization(IML)techniques require large,densely annotated sets of forged images.This requirement greatly increases labeling costs and limits a model’s ability to handle manipulation types that are novel or absent from the training data.To address these issues,we present CLIP-IML,an IML framework that leverages contrastive language-image pre-training(CLIP).A lightweight feature-reconstruction module transforms CLIP token sequences into spatial tensors,after which a compact feature-pyramid network and a multi-scale fusion decoder work together to capture information from fine to coarse levels.We evaluated CLIP-IML on ten public datasets that cover copy-move,splicing,removal,and artificial intelligence(AI)-generated forgeries.The framework raises the average F1-score by 7.85%relative to the strongest recent baselines and secures either the first-or second-place performance on every dataset.Ablation studies show that CLIP pre-training,higher resolution inputs,and the multi-scale decoder each make complementary contributions.Under six common post-processing perturbations,as well as the compression pipelines used by Facebook,Weibo,and WeChat,the performance decline never exceeds 2.2%,confirming strong practical robustness.Moreover,CLIP-IML requires only a few thousand annotated images for training,which markedly reduces data-collection and labeling effort compared with previous methods.All of these results indicate that CLIP-IML is highly generalizable for image tampering localization across a wide range of tampering scenarios.展开更多
A new method ,which is called image manipulation, is introduced to analyze the cavitation of flow field for the first time. As the complexity of the cavitation development must be considering, only the method of ima...A new method ,which is called image manipulation, is introduced to analyze the cavitation of flow field for the first time. As the complexity of the cavitation development must be considering, only the method of image manipulation can calculate the strength of the cavitation more accurately. This method based on wavelet transform is used to eliminate the noise. The area of the cavitations is deduced to serve as the strength of cavitation. The method is applied in an example of inducer's rotating cavitation. The results show that using image manipulation can get the accurate date of cavitation with ease,and the reason of the inducer shaft's vibration is uncovered clearly.展开更多
Episodes of tectonic activities since Archaean time in one of the oldest craton,the eastern Yilgarn Craton of Western Australia,have left a complex pattern in the architectural settings.Insights of the crustal scale a...Episodes of tectonic activities since Archaean time in one of the oldest craton,the eastern Yilgarn Craton of Western Australia,have left a complex pattern in the architectural settings.Insights of the crustal scale architectural settings of the Craton have been made through geophysical data modelling and imaging using high resolution aeromagnetic and Bouguer gravity data.The advanced technique of image processing using pseudocolour composition,hill-shading and the multiple data layers compilation in the hue,saturation and intensity(HSI)space has been used for image based analysis of potential field data.Geophysical methods of anomaly enhancement technique along with the imaging technique are used to delineate several regional and as well as local structures.Multiscale analysis in geophysical data processing with the application of varying upward continuation levels,and also anomaly enhancement techniques using spatial derivatives are used delineating major shear zones and regional scale structures.A suitable data based interpretation of basement architecture of the study area is given.展开更多
In the field of image forensics,image tampering detection is a critical and challenging task.Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation,...In the field of image forensics,image tampering detection is a critical and challenging task.Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of tampering.Although deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational efficiency.To address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection techniques.In contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise stream.This design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level interaction.Furthermore,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region detection.Compared with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k datasets.In addition,it has been extensively validated on other datasets,including CASIA and DIS25k.Experimental results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.展开更多
The pervasive use of photo editing applications such as Photoshop and FaceTune has significantly altered societal beauty standards, particularly for individuals with skin of color, often leading to unrealistic expecta...The pervasive use of photo editing applications such as Photoshop and FaceTune has significantly altered societal beauty standards, particularly for individuals with skin of color, often leading to unrealistic expectations regarding skin appearance and health. These tools allow users to smooth skin textures, lighten skin tones, and erase imperfections, perpetuating Eurocentric beauty ideals that frequently marginalize the natural diversity of skin tones and textures. Consequently, individuals with skin of color may seek dermatological interventions—such as skin lightening treatments, aggressive acne scar revisions, and other cosmetic procedures—aimed at achieving appearances that align more closely with digitally manipulated images. This pursuit of an unattainable aesthetic can result in increased dissatisfaction with common skin conditions like hyperpigmentation and keloids, which are often misrepresented in edited photos. Additionally, the psychological impact of these alterations can exacerbate feelings of inadequacy, contributing to conditions such as anxiety and body dysmorphic disorder. Dermatologists face the dual challenge of addressing patients’ clinical needs while also managing their expectations shaped by digital enhancements. To combat this, it is essential for dermatologists to integrate patient education that emphasizes the beauty of diverse skin tones and the discrepancies between digital images and authentic skin health. By fostering an understanding of realistic outcomes and promoting the acceptance of natural skin characteristics, dermatologists can empower individuals with skin of color to prioritize authentic skin health over digitally influenced ideals, ultimately leading to more satisfying dermatological care and improved self-image.展开更多
A galvanometric scanner with a dynamic focus was designed using a PC to realize the model transform of the image, calculate the interpolation points of the image, and implement the focus compensation of the dynamic fo...A galvanometric scanner with a dynamic focus was designed using a PC to realize the model transform of the image, calculate the interpolation points of the image, and implement the focus compensation of the dynamic focus system. The interrupt of PC was used for the real-time control. It was confirmed that the PC-based galvanometric scanner with dynamic focus could run more than 72 h stably, with an accuracy of 100 ±0.1 mm, and the period of real-time control was less than 20μs.展开更多
The computer graphics and computer vision communities have been working closely together in recent years and a variety of algorithms and applications have been developed to analyze and manipulate the visual media arou...The computer graphics and computer vision communities have been working closely together in recent years and a variety of algorithms and applications have been developed to analyze and manipulate the visual media around us. There are three major driving forces behind this phenomenon： 1） the availability of big data from the Internet has created a demand for dealing with the ever-increasing, vast amount of resources; 2） powerful processing tools, such as deep neural networks, provide effective ways for learning how to deal with heterogeneous visual data; 3） new data capture devices, such as the Kilxect, the bridge betweea algorithms for 2D image understanding and 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey recent research on how computer vision techniques benefit computer graphics techniques and vice versa, and cover research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest possible further research directions.展开更多
One-shot face reenactment is a challenging task due to the identity mismatch between source and driving faces.Most existing methods fail to completely eliminate the interference of driving subjects’identity informati...One-shot face reenactment is a challenging task due to the identity mismatch between source and driving faces.Most existing methods fail to completely eliminate the interference of driving subjects’identity information,which may lead to face shape distortion and undermine the realism of reenactment results.To solve this problem,in this paper,we propose using a 3D morphable model(3DMM)for explicit facial semantic decomposition and identity disentanglement.Instead of using 3D coefficients alone for reenactment control,we take advantage of the generative ability of 3DMM to render textured face proxies.These proxies contain abundant yet compact geometric and semantic information of human faces,which enables us to compute the face motion field between source and driving images by estimating the dense correspondence.In this way,we can approximate reenactment results by warping source images according to the motion field,and a generative adversarial network(GAN)is adopted to further improve the visual quality of warping results.Extensive experiments on various datasets demonstrate the advantages of the proposed method over existing state-of-the-art benchmarks in both identity preservation and reenactment fulfillment.展开更多
The capability to image, as well as control and manipulate single molecules such as nucleic acids(DNA or RNA) can greatly enrich our knowledge of the roles of individual biomolecules in cellular processes and their be...The capability to image, as well as control and manipulate single molecules such as nucleic acids(DNA or RNA) can greatly enrich our knowledge of the roles of individual biomolecules in cellular processes and their behavior in native environments. Here we summarize the recent advances of single nucleic acid imaging based on optical observation and force manipulation. We start by discussing the superiority of single molecule image, the central roles nucleic acids play in biosystems, and the significance of single molecule image towards nucleic acids. We then list a series of representative examples in brief to illustrate how nucleic acid of various morphologies has been imaged from different aspects, and what can be learned from such characterizations. Finally,concluding remarks on parts of which should be improved and outlook are outlined.展开更多
基金National Natural Science Foundation of China(61703363)Shanxi Provincial Basic Research Program(202403021221206)+2 种基金Key Project of Shanxi Provincial Strategic Research on Science and Technology(202304031401011)Funding Project for Scientific Research Innovation Team on Data Mining and Industrial Intelligence Applications(YCXYTD-202402)Yuncheng University Research Project(YQ-2020021)。
文摘In existing image manipulation localization methods,the receptive field of standard convolution is limited,and during feature transfer,it is easy to lose high-frequency information about traces of manipulation.In addition,during feature fusion,the use of fixed sampling kernels makes it difficult to focus on local changes in features,leading to limited localization accuracy.This paper proposes an image manipulation localization method based on dual-branch hybrid convolution.First,a dual-branch hybrid convolution module is designed to expand the receptive field of the model to enhance the feature extraction ability of contextual semantic information,while also enabling the model to focus more on the high-frequency detail features of manipulation traces while localizing the manipulated area.Second,a multiscale content-aware feature fusion module is used to dynamically generate adaptive sampling kernels for each position in the feature map,enabling the model to focus more on the details of local features while locating the manipulated area.Experimental results on multiple datasets show that this method not only effectively improves the accuracy of image manipulation localization but also enhances the robustness of the model.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under Grant No.2023D01C21the National Natural Science Foundation of China under Grant No.62362063.
文摘Existing image manipulation localization(IML)techniques require large,densely annotated sets of forged images.This requirement greatly increases labeling costs and limits a model’s ability to handle manipulation types that are novel or absent from the training data.To address these issues,we present CLIP-IML,an IML framework that leverages contrastive language-image pre-training(CLIP).A lightweight feature-reconstruction module transforms CLIP token sequences into spatial tensors,after which a compact feature-pyramid network and a multi-scale fusion decoder work together to capture information from fine to coarse levels.We evaluated CLIP-IML on ten public datasets that cover copy-move,splicing,removal,and artificial intelligence(AI)-generated forgeries.The framework raises the average F1-score by 7.85%relative to the strongest recent baselines and secures either the first-or second-place performance on every dataset.Ablation studies show that CLIP pre-training,higher resolution inputs,and the multi-scale decoder each make complementary contributions.Under six common post-processing perturbations,as well as the compression pipelines used by Facebook,Weibo,and WeChat,the performance decline never exceeds 2.2%,confirming strong practical robustness.Moreover,CLIP-IML requires only a few thousand annotated images for training,which markedly reduces data-collection and labeling effort compared with previous methods.All of these results indicate that CLIP-IML is highly generalizable for image tampering localization across a wide range of tampering scenarios.
文摘A new method ,which is called image manipulation, is introduced to analyze the cavitation of flow field for the first time. As the complexity of the cavitation development must be considering, only the method of image manipulation can calculate the strength of the cavitation more accurately. This method based on wavelet transform is used to eliminate the noise. The area of the cavitations is deduced to serve as the strength of cavitation. The method is applied in an example of inducer's rotating cavitation. The results show that using image manipulation can get the accurate date of cavitation with ease,and the reason of the inducer shaft's vibration is uncovered clearly.
基金supported by Spaceage Geoconsulting,a research oriented consulting firm.
文摘Episodes of tectonic activities since Archaean time in one of the oldest craton,the eastern Yilgarn Craton of Western Australia,have left a complex pattern in the architectural settings.Insights of the crustal scale architectural settings of the Craton have been made through geophysical data modelling and imaging using high resolution aeromagnetic and Bouguer gravity data.The advanced technique of image processing using pseudocolour composition,hill-shading and the multiple data layers compilation in the hue,saturation and intensity(HSI)space has been used for image based analysis of potential field data.Geophysical methods of anomaly enhancement technique along with the imaging technique are used to delineate several regional and as well as local structures.Multiscale analysis in geophysical data processing with the application of varying upward continuation levels,and also anomaly enhancement techniques using spatial derivatives are used delineating major shear zones and regional scale structures.A suitable data based interpretation of basement architecture of the study area is given.
基金supported by National Natural Science Foundation of China(No.61502274).
文摘In the field of image forensics,image tampering detection is a critical and challenging task.Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of tampering.Although deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational efficiency.To address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection techniques.In contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise stream.This design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level interaction.Furthermore,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region detection.Compared with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k datasets.In addition,it has been extensively validated on other datasets,including CASIA and DIS25k.Experimental results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.
文摘The pervasive use of photo editing applications such as Photoshop and FaceTune has significantly altered societal beauty standards, particularly for individuals with skin of color, often leading to unrealistic expectations regarding skin appearance and health. These tools allow users to smooth skin textures, lighten skin tones, and erase imperfections, perpetuating Eurocentric beauty ideals that frequently marginalize the natural diversity of skin tones and textures. Consequently, individuals with skin of color may seek dermatological interventions—such as skin lightening treatments, aggressive acne scar revisions, and other cosmetic procedures—aimed at achieving appearances that align more closely with digitally manipulated images. This pursuit of an unattainable aesthetic can result in increased dissatisfaction with common skin conditions like hyperpigmentation and keloids, which are often misrepresented in edited photos. Additionally, the psychological impact of these alterations can exacerbate feelings of inadequacy, contributing to conditions such as anxiety and body dysmorphic disorder. Dermatologists face the dual challenge of addressing patients’ clinical needs while also managing their expectations shaped by digital enhancements. To combat this, it is essential for dermatologists to integrate patient education that emphasizes the beauty of diverse skin tones and the discrepancies between digital images and authentic skin health. By fostering an understanding of realistic outcomes and promoting the acceptance of natural skin characteristics, dermatologists can empower individuals with skin of color to prioritize authentic skin health over digitally influenced ideals, ultimately leading to more satisfying dermatological care and improved self-image.
文摘A galvanometric scanner with a dynamic focus was designed using a PC to realize the model transform of the image, calculate the interpolation points of the image, and implement the focus compensation of the dynamic focus system. The interrupt of PC was used for the real-time control. It was confirmed that the PC-based galvanometric scanner with dynamic focus could run more than 72 h stably, with an accuracy of 100 ±0.1 mm, and the period of real-time control was less than 20μs.
基金This research was sponsored by the National Natural Science Foundation of China under Grant Nos. 61572264 and 61373069, the National Key Research and Development Plan of China under Grant No. 2016YFB1001402, Huawei Innovation Research Program （HIRP）, China Association for Science and Technology （CAST） Young Talents Plan, and Tianjin Short-Term Recruitment Program of Foreign Experts.
文摘The computer graphics and computer vision communities have been working closely together in recent years and a variety of algorithms and applications have been developed to analyze and manipulate the visual media around us. There are three major driving forces behind this phenomenon： 1） the availability of big data from the Internet has created a demand for dealing with the ever-increasing, vast amount of resources; 2） powerful processing tools, such as deep neural networks, provide effective ways for learning how to deal with heterogeneous visual data; 3） new data capture devices, such as the Kilxect, the bridge betweea algorithms for 2D image understanding and 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey recent research on how computer vision techniques benefit computer graphics techniques and vice versa, and cover research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest possible further research directions.
基金supported in part by the Beijing Municipal Natural Science Foundation,China(No.4222054)in part by the National Natural Science Foundation of China(Nos.62276263 and 62076240)the Youth Innovation Promotion Association CAS,China(No.Y2023143).
文摘One-shot face reenactment is a challenging task due to the identity mismatch between source and driving faces.Most existing methods fail to completely eliminate the interference of driving subjects’identity information,which may lead to face shape distortion and undermine the realism of reenactment results.To solve this problem,in this paper,we propose using a 3D morphable model(3DMM)for explicit facial semantic decomposition and identity disentanglement.Instead of using 3D coefficients alone for reenactment control,we take advantage of the generative ability of 3DMM to render textured face proxies.These proxies contain abundant yet compact geometric and semantic information of human faces,which enables us to compute the face motion field between source and driving images by estimating the dense correspondence.In this way,we can approximate reenactment results by warping source images according to the motion field,and a generative adversarial network(GAN)is adopted to further improve the visual quality of warping results.Extensive experiments on various datasets demonstrate the advantages of the proposed method over existing state-of-the-art benchmarks in both identity preservation and reenactment fulfillment.
基金supported by the National Natural Science Foundation of China (21525523, 21574048, 21375042, 21405054)the National Basic Research Program of China (2015CB932600, 2013CB933000)+1 种基金the Special Fund for Strategic New Industry Development of Shenzhen, China (JCYJ20150616144425376)1000 Young Talent (to Fan Xia)
文摘The capability to image, as well as control and manipulate single molecules such as nucleic acids(DNA or RNA) can greatly enrich our knowledge of the roles of individual biomolecules in cellular processes and their behavior in native environments. Here we summarize the recent advances of single nucleic acid imaging based on optical observation and force manipulation. We start by discussing the superiority of single molecule image, the central roles nucleic acids play in biosystems, and the significance of single molecule image towards nucleic acids. We then list a series of representative examples in brief to illustrate how nucleic acid of various morphologies has been imaged from different aspects, and what can be learned from such characterizations. Finally,concluding remarks on parts of which should be improved and outlook are outlined.
