MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection

Chengbo, Dong,; Chen, Xinru; Hu, Ruohan; Cao, Juan; Li, Xirong

doi:10.1109/tpami.2022.3180556

Cited by 105 publications

(49 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method is compared with several algorithms that have been effective in spliced localization, including three traditional tampering localization methods [e.g., error level analysis (ELA), 49 NOI1, 50 and CFA 6 ] and seven deep learning methods (e.g., DeepLabv3+, 31 RRU-Net, 16 DFCN, 51 MVSS-Net++, 52 PSCC-Net, 34 SE-Net, 26 and CFL-Net 17 ). The specific experimental results are shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

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

Gan,

Jiang,

Chai

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

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 stateof-the-art methods in image forgery localization.

show abstract

Section: Resultsmentioning

confidence: 99%

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

Gan,

Jiang,

Chai

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…Image manipulation through duplication is a common problem in science [Bik22]. Even though much of the work on identifying manipulations remains manual, recent work relies on machine‐learning techniques such as CNNs [WWZ * 19, LH19, BNTZ20, YLL * 20, BCM * 21, DCH * 23, KNY * 22]. CNNs are effective for images, where large datasets can be acquired or generated.…”

Section: Related Workmentioning

confidence: 99%

Ferret: Reviewing Tabular Datasets for Manipulation

Lange

Sahai

Phillips

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

How do we ensure the veracity of science? The act of manipulating or fabricating scientific data has led to many high‐profile fraud cases and retractions. Detecting manipulated data, however, is a challenging and time‐consuming endeavor. Automated detection methods are limited due to the diversity of data types and manipulation techniques. Furthermore, patterns automatically flagged as suspicious can have reasonable explanations. Instead, we propose a nuanced approach where experts analyze tabular datasets, e.g., as part of the peer‐review process, using a guided, interactive visualization approach. In this paper, we present an analysis of how manipulated datasets are created and the artifacts these techniques generate. Based on these findings, we propose a suite of visualization methods to surface potential irregularities. We have implemented these methods in Ferret, a visualization tool for data forensics work. Ferret makes potential data issues salient and provides guidance on spotting signs of tampering and differentiating them from truthful data.

show abstract

“…To detect stitched images, Liu et al proposed an image stitching detection method based on a combination of deep neural networks and conditional random fields [20], using a trained fully convolutional network (FCN) and conditional random fields (CRF) for image forgery detection, and achieving pixel-level stitching region positioning. Dong et al proposed the MVSS-Net [21] to use the edge information of the image to avoid the performance deterioration caused by pixel-level operation detection. Wu et al proposed Mantra-Net [22], which uses feature stitching to identify local abnormal features to detect forged pixels.…”

Section: Related Workmentioning

confidence: 99%

Local anomaly detection network with textural feature enhancement

ZHENG,

LING,

HAI

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

View full text Add to dashboard Cite

MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection

Cited by 105 publications

References 45 publications

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

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

Ferret: Reviewing Tabular Datasets for Manipulation

Local anomaly detection network with textural feature enhancement

Contact Info

Product

Resources

About