Detection and Localization of Video Object Removal by Spatio-Temporal LBP Coherence Analysis

Bai, Shanshan; Yao, Haichao; Ni, Rongrong; Zhao, Yao

doi:10.1007/978-3-030-34113-8_21

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…In [96], authors utilize an analysis of spatial-temporally coherence to identify areas with unusual coherence. Later in [97] enhance the previous idea with a Local Binary pattern to better extract / detect forged areas from videos. In the latest years, method involving some deep learning methods have been suggested.…”

Section: Video Object Removal Detectionmentioning

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: Video Object Removal Detectionmentioning

confidence: 99%

Image Inpainting Forgery Detection: A Review

Barglazan,

Brad,

Constantinescu

2023

Preprint

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“…After calculating the statistical correlation of Hessian matrix, a detector was designed to differentiate the manipulated regions from original ones [23]. Recently, Zhao et al [24] also implemented a region-level detector. It firstly aligns consecutive frames, and then identifies the candidate tampered regions by spatial local binary pattern (LBP), finally combines the temporal LBP to exclude the misjudgment areas for satisfactory localization accuracy.…”

Section: Introductionmentioning

confidence: 99%

Localization of Deep Video Inpainting Based on Spatiotemporal Convolution and Refinement Network

Ding

Pan

Luo

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Deep learning-based video inpainting can fill the missing or undesired regions with spatial-temporal consistent contents without obvious visually distortion. Although the original purpose of deep inpainting is to repair flawed videos, it can also be adopted for malicious purposes, e.g., removal of specific objects. Therefore, automatically locating the inpainted regions is a challenging task in video forensics. This paper proposes a new forensic refinement framework to localize the deep inpainted regions by considering the spatial-temporal viewpoint. Firstly, we design a spatiotemporal convolution to suppress redundancy for highlighting deep inpainting traces. Then, a detection module is constructed with four concatenated ResNet blocks, and two upsampling layers to achieve a rough location map. Finally, a modified U-net based refinement module is developed for the pixel-wise localization map. Deep inpaiting video datasets created by the state-of-the-art deep inpainting method, have been evaluated, and extensive experimental results clearly demonstrate the efficacy of the proposed approach.

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A comprehensive survey on passive techniques for digital video forgery detection

Shelke

Kasana

2020

Multimed Tools Appl

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Detection and Localization of Video Object Removal by Spatio-Temporal LBP Coherence Analysis

Cited by 5 publications

References 12 publications

Image Inpainting Forgery Detection: A Review

Image Inpainting Forgery Detection: A Review

Localization of Deep Video Inpainting Based on Spatiotemporal Convolution and Refinement Network

A comprehensive survey on passive techniques for digital video forgery detection

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