COVERAGE — A novel database for copy-move forgery detection

Wen, Bihan; Zhu, Ye; Subramanian, Ramanathan; Ng, Tian-Tsong; Shen, Xuanjing; Winkler, Stefan

doi:10.1109/icip.2016.7532339

Cited by 233 publications

(170 citation statements)

References 13 publications

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“…• We present a new dataset for image tamper localization task that includes a large number of images with ground-truth binary mask. This dataset is larger than current publicly available datasets such as IEEE Forensics [1] and COVERAGE [86]. It will also help train deeper networks for image tamper classification or localization tasks.…”

Section: Main Contributionsmentioning

confidence: 99%

“…NIST'16 [2] is a very challenging dataset, which includes three main types of manipulation -(a) copy-clone, (b) removal, and (c) splicing. This [2], IEEE Forensics Challenge [1] and COVERAGE [86] datasets respectively. Each curve has area under the curve (AUC), which are provided in Table 3.…”

Section: Dataset Preparationmentioning

confidence: 99%

“…We test our proposed model on three datasets-NIST'16 [2], IEEE Forensics Challenge [1] and COVERAGE [86]. We first train our model with synthesized data (discussed in Sec.…”

Section: Performance Of the Proposed Modelmentioning

confidence: 99%

“…Figs. 8(a,b) show the ROC plots for image tamper localization, on NIST'16 [2], IEEE Forensics Challenge [1], and COVERAGE [86] datasets respectively. These ROC curves measure the performance of binary pixel classification whether a pixel is manipulated or not.…”

Section: Comparison Against Existing Approachesmentioning

confidence: 99%

“…The table shows the pixel-wise accuracy on NIST'16[2], IEEE Forensics Challenge[1] and COVERAGE[86] datasets for image tamper segmentation.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

Bappy

Simons

Nataraj

et al. 2019

IEEE Trans. on Image Process.

345

207

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With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy-clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets.

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Section: Main Contributionsmentioning

confidence: 99%

Section: Dataset Preparationmentioning

confidence: 99%

“…We test our proposed model on three datasets-NIST'16 [2], IEEE Forensics Challenge [1] and COVERAGE [86]. We first train our model with synthesized data (discussed in Sec.…”

Section: Performance Of the Proposed Modelmentioning

confidence: 99%

Section: Comparison Against Existing Approachesmentioning

confidence: 99%

“…The table shows the pixel-wise accuracy on NIST'16[2], IEEE Forensics Challenge[1] and COVERAGE[86] datasets for image tamper segmentation.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

Bappy

Simons

Nataraj

et al. 2019

IEEE Trans. on Image Process.

345

207

View full text Add to dashboard Cite

show abstract

How to Reconstruct the History of a Digital Image, and of Its Alterations

Bammey¹,

Colom²,

Ehret³

et al. 2022

Multimedia Security 1

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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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COVERAGE — A novel database for copy-move forgery detection

Cited by 233 publications

References 13 publications

Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

How to Reconstruct the History of a Digital Image, and of Its Alterations

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

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