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MSA-Net: Multi-scale attention network for image splicing localization

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第7期83卷 20587-20604页

作者： Yan, Caiping Wei, Huajian Lan, Zhi Li, Hong Hangzhou Normal Univ Dept Comp Sci Hangzhou 311121 Peoples R China Hangzhou InsVis Technol Co Ltd Hangzhou 311121 Peoples R China

In this paper, we introduce the novel Multi-Scale Attention Network (MSA-Net) to address the challenge of locating diverse types and sizes of splicing forgery objects. Previous methods neglect crucial characteristics of global dependencies and size, resulting in imprecise localization on background tampering and small target tampering. To overcome this, we integrate a multi-scale self-attention mechanism to capture global dependencies and fully understand the relationships between spliced objects and untampered areas. Our approach involves inserting multi-scale attention modules that combine the position attention and channel attention modules between convolution layers for feature extraction. The position attention module emphasizes spatial interdependencies, capturing relationships between feature positions. Similarly, the channel attention module captures relationships between channel features. This allows for the preservation of intrinsic details while capturing long-range semantic dependencies, which is beneficial to the detection of splicing forgery objects. Meanwhile, by dividing the feature maps into multiple sub-regions or sub-channels, our attention modules can better preserve the details while capturing long-range semantic information dependencies. Experimental results show that the proposed MSA-Net outperforms several state-of-the-art algorithms with an F1-score of 60.5% and an IOU value of 58.8% on the CASIA dataset.

关键词： image forensics Spatial-channel relationships Multi-scale Self-attention image splicing localization

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Color image splicing localization Based on Block Classification Using Transition Probability Matrix

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期129卷 1893-1919页

作者： Yildirim, Esra Odabas Tahaoglu, Gul Ulutas, Guzin Ustubioglu, Beste Nabiyev, Vasif Ataturk Univ Fac Engn Dept Software Engn TR-25240 Erzurum Turkiye Karadeniz Tech Univ Fac Engn Dept Comp Engn TR-61080 Trabzon Turkiye

With the increasing technology, digital images have become a widely used data type in crucial areas such as medical journalism and law. Since it is used in such important areas, it has become questionable whether digital images are original or not. image splicing forgery is one of the most common forgery types applied to digital images. This work proposes a new image splicing detection and localization method. Our motivation is to reveal the boundaries of forgery by using statistical features of the image blocks. The proposed method has two main stages: training and localizing. In both phases, image blocks that contain edge information are used because the splicing operation causes some inconsistency on the edges. In the training stage, original blocks are selected from the regions that include original boundaries, and forged blocks are selected from the areas that contain splicing operation-induced edges. Transition probability matrices are calculated in eight directions to obtain the correlation of the borders between the neighbor blocks on original and splicing edges. These matrices are used as a feature for each block. The blocks are classified as authentic and spliced using SVM. A new post-processing step has been proposed to eliminate the false positives that may occur due to the presence of original regions that are likely to be detected as spliced edges in the image. The publicly available Columbia dataset has been used to show the effectiveness of the state-of-the-art and proposed method. The results indicate that the proposed method has performed well even under JPEG compression and Gaussian blurring attacks.

关键词： image splicing detection image splicing localization Transition probability matrices image forgery detection

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Multi-Task SE-Network for image splicing localization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第7期32卷 4828-4840页

作者： Zhang, Yulan Zhu, Guopu Wu, Ligang Kwong, Sam Zhang, Hongli Zhou, Yicong Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Shenzhen 518055 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China

image splicing can be easily used for illegal activities such as falsifying propaganda for political purposes and reporting false news, which may result in negative impacts on society. Hence, it is highly required to detect spliced images and localize the spliced regions. In this work, we propose a multi-task squeeze and excitation network (SE-Network) for splicing localization. The proposed network consists of two streams, namely label mask stream and edge-guided stream, both of which adopt convolutional encoder-decoder architecture. The information from the edge-guided stream is transmitted to the label mask stream for enhancing the discrimination of features between the spliced and host regions. This work has three main contributions. First, image edges, along with label masks and mask edges, are exploited to supply more comprehensive supervision for the localization of spliced regions. Second, the low-level feature maps extracted from shallow layers are fused with the high-level feature maps from deep layers to provide more reliable feature for splicing localization. Finally, several squeeze and excitation attention modules are incorporated into the network to recalibrate the fused features to enhance the feature expression. Extensive experiments show that the proposed multi-task SE-Network outperforms existing splicing localization methods evidently on two synthetic splicing datasets and four benchmark splicing datasets.

关键词： splicing Location awareness image edge detection Feature extraction Streaming media Forgery Transform coding image forensics image splicing localization multi-task learning squeeze and excitation attention module low-level feature fusion

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A Multi-Stream Fusion Network for image splicing localization 29th

A Multi-Stream Fusion Network for Image Splicing Localizatio...

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29th International Conference on MultiMedia Modeling (MMM)

作者： Siopi, Maria Kordopatis-Zilos, Giorgos Charitidis, Polychronis Kompatsiaris, Ioannis Papadopoulos, Symeon Inst Informat Technol CERTH Thessaloniki 60361 Greece

ISBN: (纸本)9783031278174;9783031278181

In this paper, we address the problem of image splicing localization with a multi-stream network architecture that processes the raw RGB image in parallel with other handcrafted forensic signals. Unlike previous methods that either use only the RGB images or stack several signals in a channel-wise manner, we propose an encoder-decoder architecture that consists of multiple encoder streams. Each stream is fed with either the tampered image or handcrafted signals and processes them separately to capture relevant information from each one independently. Finally, the extracted features from the multiple streams are fused in the bottleneck of the architecture and propagated to the decoder network that generates the output localization map. We experiment with two handcrafted algorithms, i.e., DCT and Splicebuster. Our proposed approach is benchmarked on three public forensics datasets, demonstrating competitive performance against several competing methods and achieving state-of-the-art results, e.g., 0.898 AUC on CASIA.

关键词： image splicing localization image forensics multi-stream fusion network late fusion deep learning

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Toward Reliable Mobile CrowdSensing Data Collection: image splicing localization Overview 19

Toward Reliable Mobile CrowdSensing Data Collection: Image S...

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19th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Abou-Zbiba, Wahiba Benbrahim, Houda El Bakkali, Hanan El Gadi, Hajar Maqour, Zaina Benhaddou, Driss Mohammed V Univ IRDA ENSIAS Rabat Morocco Mohammed V Univ SSL ENSIAS Rabat Morocco Univ Houston Engn Technol Dept Houston TX 77204 USA Alfaisal Univ Dept Elect Engn Coll Engn Riyadh Saudi Arabia

ISBN: (纸本)9798350333398

With the advancement of technology, the collection of data used in Intelligent Transportation Systems has become increasingly easy, notably with the emergence of the Mobile CrowdSensing paradigm. This paradigm could provide insights into traffic situation, road condition, pedestrians' behaviours, public transportation situation, and so on. Through the use of the powerful sensors in the mobile, different types of data can be generated, such as Gyroscope data, light sensors data, Magnetometer data, GPS data, Accelerometer data, videos, and images. However, the use of MCS raises issues of data reliability, as it involves the participation of several mobile owners with different 'levels' of trustworthiness. An important related issue is image forgery, i.e. the manipulation of images in order to deliberately provide misleading information, which constitutes a threat to the decision-makers of the MSC-based applications in Intelligent Transportation Systems. In this paper, an overview on image forgery is presented. We examine the workflows, approaches and techniques employed for image forgery detection and localization. Then, we provide a brief review of some image splicing localization techniques. Finally, we provide a comparative analysis of handcrafted feature extraction-based techniques and deep learning-based techniques. The aim of this paper is to draw attention to the image forgery problem that could threaten any Mobile CrowdSensing application dedicated to image collection, and to provide an overview of different existing techniques that could be used to overcome this problem.

关键词： Intelligent Transportation Systems Mobile CrowdSensing image Forgery image splicing localization

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End-to-end image splicing localization based on multi-scale features and residual refinement module

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JOURNAL OF ELECTRONIC IMAGING 2023年第6期32卷 063010-063010页

作者： Gan, Zhihua Jiang, Wenbin Chai, Xiuli Song, Yalin Yu, Junyang Henan Univ Inst Intelligent Network Syst Intelligent Data Proc Engn Res Ctr Henan Prov Sch Software Kaifeng Peoples R China Henan Univ Henan Engn Res Ctr Ind Internet Things Sch Artificial Intelligence Zhengzhou Peoples R China

The addition of objects to the original image in splice forgery results in a change in the semantics of the original image, and distribution of these spliced images may bring negative impacts. To solve this issue, many forgery detection methods based on convolutional neural networks are presented. However, they tend to extract deep features, but ignore the importance of shallow semantics. In addition, the complexity of the forgery localization task leads to insufficient accuracy in detecting smaller forged regions. Based on the above problems, an end-to-end image splicing localization network based on multi-scale features and a residual refinement module (RRM) is proposed in this work. Our approach can be roughly divided into two modules: the detection module and the RRM. First, shallow and deep features are extracted from a backbone network, and deep features are then processed by the deeper atrous spatial pyramid pooling (ASPP) module to extract multi-scale features. The deeper ASPP module uses a smaller dilation rate and light convolution, which is more suitable for detecting complex counterfeit images. Second, the shallow features are fused with the multi-scale features to complement the shallow semantic information, such as texture and edges, which further improve the robustness of the model. Finally, the detection network generates coarse prediction maps that are fed to the RRM, and the RRM optimizes these masks by smoothing the boundaries, filling small gaps, and enhancing the edge details, which improves the pixel-level segmentation results for forgery detection. Extensive experimental results on several public datasets show that this method outperforms other state-of-the-art methods in image forgery localization.

关键词： image forgery detection image splicing localization multi-scale feature fusion deeper atrous spatial pyramid pooling

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image splicing localization using noise distribution characteristic

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第16期78卷 22223-22247页

作者： Zhang, Depeng Wang, Xiaofeng Zhang, Meng Hu, Jiaojiao Xian Univ Technol Xian 710048 Shaanxi Peoples R China

image splicing/compositing is common content tampering operation. In this work, we devote to improve the detection accuracy of the splicing/compositing attack for image, and propose an effective image splicing localization method based on the noise distribution characteristic in image. Firstly, the test image is divided into non-overlapping blocks by using an improved simple linear iterative clustering (SLIC) algorithm. Then block-wise local noise level estimation and noise distribution characteristic estimation are performed to generate distinguishing features. Utilizing the fact that image regions from different sources tend to have larger inter-class difference, the fuzzy c-means clustering is used to identify spliced regions. Compared to existing noise-based image splicing detection methods, experimental results on different datasets have shown that the proposed method has superior performance, especially when the noise difference between the spliced region and the original region is small. Moreover, the proposed method is robust for content-preserving manipulations.

关键词： image splicing detection image splicing localization Simple linear iterative clustering Noise distribution characteristic Fuzzy c-means clustering

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image splicing localization via Semi-global Network and Fully Connected Conditional Random Fields 1

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15th European Conference on Computer Vision (ECCV)

作者： Cun, Xiaodong Pun, Chi-Man Univ Macau Taipa Macau Peoples R China

ISBN: (数字)9783030110123

ISBN: (纸本)9783030110123;9783030110116

We address the problem of image splicing localization: given an input image, localizing the spliced region which is cut from another image. We formulate this as a classification task but, critically, instead of classifying the spliced region by local patch, we leverage the features from whole image and local patch together to classify patch. We call this structure Semi-Global Network. Our approach exploits the observation that the spliced region should not only highly relate to local features (spliced edges), but also global features (semantic information, illumination, etc.) from the whole image. Furthermore, we first integrate Fully Connected Conditional Random Fields as post-processing technique in image splicing to improve the consistency between the input image and the output of the network. We show that our method outperforms other state-of-the-art methods in three popular datasets.

关键词： image splicing localization image forgery localization Multimedia security

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Multi-scale noise-guided progressive network for image splicing detection and localization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 257卷

作者： Zhang, Dengyong Jiang, Ningjing Li, Feng Chen, Jiaxin Liao, Xin Yang, Gaobo Ding, Xiangling Changsha Univ Sci & Technol Hunan Prov Key Lab Intelligent Proc Big Data Trans Changsha 410114 Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Univ Sci & Technol Sch Comp & Commun Engn Xiangtan 411201 Hunan Peoples R China

image splicing can easily be used in illegal activities, which may have negative impact on society. Therefore, detecting splicing images and precisely localizing tampered regions are challenging tasks. In this work, we propose a dual-branch Multi-Scale Noise-Guided Progressive Network (MSNP-Net). The multi-resolution branch extracts deep semantic features of images while suppressing redundant noise. A multi-scale noise-guided branch is designed to capture more subtle tampering artifacts and guide the network to strengthen the learning of spatial structure features. The two branches complement and restrict each other. The features of different scales are fused in a gradual mechanism, and the feature expression is aggregated through the spatial channel feature aggregation module (SCAM). A large number of experiments show that the MSNP-Net proposed in this paper has better completeness and lower false alarm rate for the detection and localization of spliced images. It can achieve more refined detection results, and has good stability and robustness. Significantly outperforms other state-of-the-art methods. Our source code is available at https://***/Swag-Jiang/MSNP-Net.

关键词： image splicing localization Progressive network Multi-scale noise-guided Feature aggregation

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image splicing localization Using SLIC and Noise Level Estimation

Image Splicing Localization Using SLIC and Noise Level Estim...

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2019年上海市“智能计算与智能电网”研究生学术论坛

作者： Siqian LI Weimin WEI Xueling CHU Xiuru HUA College of Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China

With the development of computer technology and artificial intelligence technology,the authenticity of images has been seriously ***,how to determine the authenticity of images has become an important research direction in the field of digital *** present,image splicing is one of the most common methods of image *** sources of images have different noise levels,and this paper proposes a method to locate the image splicing tamper region by detecting the inconsistency of the local noise level of the ***,the image to be detected is segmented into pixel blocks with similar features by using SLIC superpixel segmentation ***,the noise level estimation method based on principal component analysis is used to calculate the local noise level of each image ***,using three clustering algorithms to cluster the results of the estimated noise level,and the splicing tamper region of the image is located according to the clustering *** experiment results show that the proposed method in this paper can effectively locate the splicing tamper region and retain more edge information of the detected region.

关键词： image splicing localization noise estimation superpixel segmentation algorithm clustering image forensics

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