An End-to-End Dense-InceptionNet for Image Copy-Move Forgery Detection

Zhong, Junliu; Pun, Chi‐Man

doi:10.1109/tifs.2019.2957693

Cited by 122 publications

(66 citation statements)

References 34 publications

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“…Zhong and Pun [23] proposed to use DenseNet instead of CNN to detect copy-move tampering. Their method also relies on a module of self-correlation computation.…”

Section: Deep-learning-based Approachesmentioning

confidence: 99%

“…Details of transformation of [24] from splicing to copy-move detector can be found in [2]. We also report results extracted from the original papers of two recent deep-learning-based methods: the DenseNet-based method in [23] and the method in [25] which is based on the so-called AR-Net (Adaptive attention and Residual refinement Network). We were unable to reproduce the lower scores reported in [2] for DF-CMFD [13], but our results (the column of "DF-CMFD -Default HP" in Table 1) are coherent with reported performance of DF-CMFD in [23] (Table III of that paper, column of "PM").…”

Section: Df-cmfd As First-stage Detectormentioning

confidence: 99%

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Disentangling copy-moved source and target areas

Darmet

Wang

Cayre

2021

Applied Soft Computing

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Copy-move is a very popular image falsification where a semantically coherent part of the image, the source area, is copied and pasted at another position within the same image as the so-called target area. The majority of existing copy-move detectors search for matching areas and thus identify the source and target zones indifferently, while only the target really represents a tampered area. To the best of our knowledge, at the moment of preparing this paper there has been only one published method called BusterNet that is capable of performing source and target disambiguation by using a specifically designed deep neural network. Different from the deep-learning-based BusterNet method, we propose in this paper a source and target disentangling approach based on local statistical model of image patches. Our proposed method acts as a secondstage detector after a first stage of copy-move detection of duplicated areas. We had the following intuition: even if no manipulation (e.g., scaling and rotation) is added on target area, its boundaries should expose a statistical deviation from the pristine area and the source area; further, if the target area is manipulated, the deviation should appear not only on the boundaries but on the full zone. Our method relies on machine learning tool with Gaussian Mixture Model to describe likelihood of image patches. Likelihoods are then compared between the pristine region and the candidate source/target areas as identified by the first-stage detector. Experiments and comparisons demonstrate the effectiveness of the proposed method.

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“…Zhong and Pun [23] proposed to use DenseNet instead of CNN to detect copy-move tampering. Their method also relies on a module of self-correlation computation.…”

Section: Deep-learning-based Approachesmentioning

confidence: 99%

Section: Df-cmfd As First-stage Detectormentioning

confidence: 99%

Disentangling copy-moved source and target areas

Darmet

Wang

Cayre

2021

Applied Soft Computing

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“…Conventional CMFD techniques with handcrafted features experience three limitations [86]. First, these techniques have an identical structure comprises of three phases.…”

Section: Deep Learning Based Cmfd Techniquesmentioning

confidence: 99%

“…First, these techniques have an identical structure comprises of three phases. Each phase is trained separately and has numerous parameters that are manually tuned [86]- [88]. Second, these techniques are mostly tuned to accomplish great performance on specific dataset(s) yet fail in other datasets [86].…”

Section: Deep Learning Based Cmfd Techniquesmentioning

confidence: 99%

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Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

Zedan¹,

Soliman²,

Elsayed³

et al. 2021

IJACSA

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Digital Image Forensics is a growing field of image processing that attempts to gain objective proof of the origin and veracity of a visual image. Copy-move forgery detection (CMFD) has currently become an active research topic in the passive/blind image forensics field. There has no doubt that conventional techniques and especially the keypoint based techniques have pushed the CMFD forward in the previous two decades. However, CMFD techniques in general and conventional techniques in particular suffer from several challenges. And thus, increasing approaches are exploiting deep learning for CMFD. In this survey, we cover the conventional and the deep learning based CMFD techniques from a new perspective.We classify the CMFD techniques into several classifications according to the detection methodology, the detection paradigm, and the detection capability . We discuss the challenges facing the CMFD techniques as well as the ways for solving them. In addition, this survey covers the evaluation metrics and datasets commonly utilized for CMFD. Also, we are debating and proposing certain plans for future research. This survey will be helpful for the researchers' as it master the recent trends of CMFD and outline some future research directions.

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Document images forgery localization using a two‐stream network

Liu

Zhu

et al. 2021

Int J of Intelligent Sys

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Document images often contain essential and sensitive information. With image editing software, one can easily manipulate the semantic meaning of the document image by copy-move, splicing, and removal, which causes many security issues. Hence, the document image forgery detection and localization are of great significance. In this paper, a novel two-stream network is proposed to detect and locate the forgery regions of document images. One stream captures forgery traces from the spatial information, including unnatural boundaries, contrast/brightness inconsistencies, and blurring. The other stream eliminates the image semantics by six residual filters, then capture the anomalous features between neighboring pixels introduced by upsampling, downsamping, and inpainting. Finally, the discriminant network is introduced to deeply fuse features from the two streams, and discriminates whether each image patch is forged or not using global average pooling and fully connected layer. Extensive experimental results on our constructed document image data set and the Security AI Challenger Program data set demonstrate the proposed two-stream network outperforms state-of-the-art methods. This proposed two-stream network is also better than each single stream, and is robust to some common forgery postprocessing operations.

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An End-to-End Dense-InceptionNet for Image Copy-Move Forgery Detection

Cited by 122 publications

References 34 publications

Disentangling copy-moved source and target areas

Disentangling copy-moved source and target areas

Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

Document images forgery localization using a two‐stream network

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