A Discriminative Multi-Channel Noise Feature Representation Model for Image Manipulation Localization

Zhou, Yang; Wang, Hongxia; Zeng, Qiang; Zhang, Rui; Meng, Sijiang

doi:10.1109/icassp49357.2023.10096501

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The problem still with their approach is that the mask data used for the inpainting process is generated randomly, not in a realistic approach. In the same category as IID-NET methods, others have suggested [88] incorporating more enhancement blocks to make the detection more reliable and for a more general range of forgeries, and they used several datasets some of which present not very realistic tampering (more on the datasets on the next chapter).…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

Image Inpainting Forgery Detection: A Review

Barglazan,

Brad,

Constantinescu

2023

Preprint

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In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods, as well as those based on neural networks. Furthermore, we will explore the artifacts associated with the identification of modified photos and present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all of the above, the final outcome of this study is to provide a comprehensive perspective on the abilities and constraints to detect images for which an inpainting object removal method was applied.

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Section: Machine Learning-based Methodsmentioning

confidence: 99%

Image Inpainting Forgery Detection: A Review

Barglazan,

Brad,

Constantinescu

2023

Preprint

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“…FREQUENCY ENHANCED ATTENTION AND OVERALL OBJECTIVES Frequency Enhanced Attention. Inspired by the previous works [6], [66], [67] filter [10] has demonstrated its effectiveness in extracting high-frequency signals, we employ it on input images and features to highlight frequency-importance features. As shown in Fig.…”

Section: A Contrastive Learning Configurationmentioning

confidence: 99%

Multi-View Contrastive Learning with Virtual Social Group Influence for Social Recommendation

Zhang,

Li,

Zhang

2023

Preprint

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“…The problem with their approach is still that the mask data used for the inpainting process is generated randomly and not in a realistic manner. In the same category as the IID-NET methods, others have suggested [82] incorporating more enhancement blocks to make the detection more reliable, and for a more general range of forgeries, they used several datasets some of which present not very realistic tampering (more on the datasets in the next chapter).…”

mentioning

confidence: 99%

Image Inpainting Forgery Detection: A Review

Barglazan,

Brad,

Constantinescu

2024

J. Imaging

View full text Add to dashboard Cite

In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods as well as those based on neural networks. Furthermore, we will present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all the above, the outcome of this study is to provide a comprehensive perspective on the abilities and constraints of detecting object removal via the inpainting procedure in images.

show abstract

A Discriminative Multi-Channel Noise Feature Representation Model for Image Manipulation Localization

Cited by 3 publications

References 17 publications

Image Inpainting Forgery Detection: A Review

Image Inpainting Forgery Detection: A Review

Multi-View Contrastive Learning with Virtual Social Group Influence for Social Recommendation

Image Inpainting Forgery Detection: A Review

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