DFST-UNet: Dual-Domain Fusion Swin Transformer U-Net for Image Forgery Localization [PDF]
EntropyImage forgery localization is critical in defending against the malicious manipulation of image content, and is attracting increasing attention worldwide. In this paper, we propose a Dual-domain Fusion Swin Transformer U-Net (DFST-UNet) for image forgery
Jianhua Yang +3 more
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An Efficient CNN Model to Detect Copy-Move Image Forgery
IEEE Access, 2022 Recently, digital images have become used in many applications, where they have become the focus of digital image processing researchers. Image forgery represents one hot topic on which researchers prioritize their studies.
Khalid M. Hosny +3 more
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Frequency domain manipulation of multiple copy-move forgery in digital image forensics. [PDF]
PLoS ONECopy move forgery is a type of image forgery in which a portion of the original image is copied and pasted in a new location on the same image. The consistent illumination and noise pattern make this kind of forgery more difficult to detect. In copy-move
Tanzeela Qazi +5 more
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Method for Image Forgery Detection Based on Deformable Self-Correlation Network [PDF]
Jisuanji gongcheng, 2021 The deep learning-based copy-move forgery detection methods ignore the spatial layout of the features, leading to a reduction in the detection performance for small-region forgery samples.Additionally,the fixed size of the receptive fields in ...
LIANG Peng, WU Yuting, ZHAO Huimin, LI Chunying, HE Wa, LI Shaofa
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Self-Adversarial Training Incorporating Forgery Attention for Image Forgery Localization [PDF]
IEEE Transactions on Information Forensics and Security, 2022 Image editing techniques enable people to modify the content of an image without leaving visual traces and thus may cause serious security risks. Hence the detection and localization of these forgeries become quite necessary and challenging. Furthermore, unlike other tasks with extensive data, there is usually a lack of annotated forged images for ...
Long Zhuo +3 more
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Image Forgery Detection with Interpretability
CoRR, 2022 In this work, we present a learning based method focusing on the convolutional neural network (CNN) architecture to detect these forgeries. We consider the detection of both copy-move forgeries and inpainting based forgeries. For these, we synthesize our own large dataset.
Ankit Katiyar, Arnav Bhavsar
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Unveiling Copy-Move Forgeries: Enhancing Detection With SuperPoint Keypoint Architecture
IEEE Access, 2023 The authentication of digital images poses a significant challenge due to the wide range of image forgery techniques employed, with one notable example being a copy-move forgery.
Anjali Diwan +4 more
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Seamless Copy–Move Replication in Digital Images
Journal of Imaging, 2022 The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries.
Tanzeela Qazi +3 more
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Image copy-move forgery detection and localization based on super-BPD segmentation and DCNN
Scientific Reports, 2022 With the increasing importance of image information, image forgery seriously threatens the security of image content. Copy-move forgery detection (CMFD) is a greater challenge because its abnormality is smaller than other forgeries.
Qianwen Li +3 more
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Structural Correlation Based Method for Image Forgery Classification and Localization
Applied Sciences, 2020 In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying ...
Nam Thanh Pham +2 more
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