Effective image tampering localization with multi-scale ConvNeXt feature fusion

Zhu, Haochen; Cao, Gang; Zhao, Mo; Tian, Huawei; Lin, Weiguo

doi:10.1016/j.jvcir.2023.103981

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…CAT‐Net (https://github.com/mjkwon2021/CAT‐Net) [ 31 ] is a modified network based on HR‐Net and considers RGB and DCT domains simultaneously to effectively learn forensic features.…”

Section: Methodsmentioning

confidence: 99%

“…Numerous networks designed for localizing image manipulations typically adopt an encoder‐decoder structure. [ 14,16,19,21,25,30,31 ] These frameworks utilize backbone networks to extract features, which are subsequently fed into a segmentation network. Within the encoder, a variety of feature extraction streams are deployed, each tailored to process a particular type of data.…”

Section: Related Workmentioning

confidence: 99%

“…[ 32 ] Bayar convolution, introduced by Belhassen Bayar, also contributes to the inconsistent noise detection. [ 14 ] Single‐stream approaches [ 14,19,30,31 ] may overlook critical data, prompting some researchers to adopt multi‐stream strategies that promise a more comprehensive and robust analysis. [ 16,21,25,29,33 ] In these multi‐stream approaches, each stream operates autonomously to compute and extract features, leaving the potential for cross‐backbone feature fusion largely unexplored.…”

Section: Related Workmentioning

confidence: 99%

“…Numerous current methods in IMLT tend to favor deep‐layer features, particularly those derived from the terminal layers of backbone models. [ 16,19,20,25,32,33 ] This preference can lead to a neglect of valuable data, given that shallow and mid‐layer features often have a wealth of indicators critical for identifying forgeries [ 28 ] While recent studies have started to acknowledge the importance of shallow features, [ 31 ] their utilization remains basic, with a lack of in‐depth exploration into methods for enhancing these essential features.…”

Section: Related Workmentioning

confidence: 99%

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Dual‐Stream Fusion and Multi‐scale Analysis: Introducing the Synergistic Dual‐Stream Network (SDS‐Net) for Image Manipulation Segmentation

Song,

Lin,

Xie

2024

Advanced Intelligent Systems

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This article introduces the synergistic dual‐stream network (SDS‐Net), a novel neural network architecture that significantly enhances the detection of image manipulations. SDS‐Net employs a unique dual‐stream fusion strategy that processes both RGB image and the corresponding noise map. It innovatively combines features computation from the different blocks of dual backbones, and leverages a multi‐scale spatial pyramid pooling (MSPP) module to expand the receptive fields of shallow features. This approach not only enriches the feature representation, but also ensures the precise localization of manipulated regions. Extensive experiments conducted on various public datasets demonstrate the superiority of SDS‐Net over several state‐of‐the‐art methods.

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“…CAT‐Net (https://github.com/mjkwon2021/CAT‐Net) [ 31 ] is a modified network based on HR‐Net and considers RGB and DCT domains simultaneously to effectively learn forensic features.…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Dual‐Stream Fusion and Multi‐scale Analysis: Introducing the Synergistic Dual‐Stream Network (SDS‐Net) for Image Manipulation Segmentation

Song,

Lin,

Xie

2024

Advanced Intelligent Systems

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“…In recent years, substantial achievements have been made in image tampering localization techniques. [4][5][6][7][8] In contrast, video forensics have received less attention. The traditional artificial feature approaches rely on detecting the artifacts left by compression, 9 imaging pipeline, 10 and inter-frame motion.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal co-attention fusion network for video splicing localization

Lin,

Cao,

Lou

et al. 2024

J. Electron. Imag.

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Digital video splicing has become easy and ubiquitous. Malicious users copy some regions of a video and paste them into another video to create realistic forgeries. It is important to blindly detect such forgery regions in videos. A spatio-temporal co-attention fusion network (SCFNet) is proposed for video splicing localization. Specifically, a three-stream network is used as an encoder to capture manipulation traces across multiple frames. The deep interaction and fusion of spatio-temporal forensic features are achieved by the novel parallel and cross co-attention fusion modules. A lightweight multilayer perceptron decoder is adopted to yield a pixel-level tampering localization map. A new large-scale video splicing dataset is created for training the SCFNet. Extensive tests on benchmark datasets show that the localization and generalization performances of our SCFNet outperform the state-of-theart. Code and datasets are available at https://github.com/multimediaFor/SCFNet.

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