Iterative Copy-Move Forgery Detection Based on a New Interest Point Detector

Zandi, Mohsen; Mahmoudi-Aznaveh, Ahmad; Talebpour, Alireza

doi:10.1109/tifs.2016.2585118

Cited by 132 publications

(117 citation statements)

References 40 publications

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“…A family of algorithms, known as "active forensics" attempt to deal with image modifications by embedding watermarks in multimedia content and monitoring their integrity [13,5]. Another strand of work focuses on searching for telltale self-repetitions [51,17] or inconsistencies in the image, without making any further assumptions. With respect to image forensics, a recent survey and evaluation of algorithms dealing with these challenges was presented by Zampoglou et al [49].…”

Section: Related Workmentioning

confidence: 99%

Verification of Web Videos Through Analysis of Their Online Context

Papadopoulou

Zampoglou

Papadopoulos

et al. 2019

Video Verification in the Fake News Era

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This chapter discusses the problem of analysing the online "context" of User Generated Videos (UGVs) with the goal of extracting clues that help analysts with the video verification process. As video context, we refer to information surrounding the video i.e. information about the video itself, user comments below the video, information about the video publisher and any dissemination of the video through other video platforms or social media. As a starting point, we present the Fake Video Corpus, a dataset of debunked and verified UGVs that aims at serving as reference for qualitative and quantitative analysis and evaluation. Next, we present a web-based service, called Context Aggregation and Analysis, which supports the collection, filtering and mining of contextual pieces of information that can serve as verification signals. This service aims to assist Internet users in their video verification efforts.

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Section: Related Workmentioning

confidence: 99%

Verification of Web Videos Through Analysis of Their Online Context

Papadopoulou

Zampoglou

Papadopoulos

et al. 2019

Video Verification in the Fake News Era

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“…Polar cosine transform has the relevant better performance than that of the polar sine transform [19]. In our proposed algorithm, the even order of the RHFMs with cosine is proposed to construct the geometric invariant moments.…”

Section: The Definition Of the Rhfms With Even Ordermentioning

confidence: 99%

A Duplicated Forgery Detection Fusion Algorithm using SIFT and Radial-Harmonic Fourier Moments

Gan¹

2018

IJPE

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This paper proposed an algorithm that fused block-based algorithm (Radial Harmonic Fourier Moments, RHFMs) and SIFT algorithm (Scale Invariant Feature Transform, SIFT) for the duplicated forgery detection, also called copy-move forgery detection. It can effectively detect forgery image. Firstly, the SIFT algorithm is proposed to extract feature points from the pre-processing image. Subsequently, the nearest-neighbor (2NN) test, adaptive Euclidean distance and Random sample consensus (RANSAC) are applied to remove most of the mismatched feature points and get the candidate inlier matches. The affine matrixes based on the RANSAC are obtained by the candidate inlier matches. Then, Radial Harmonic Fourier Moments is proposed to extract invariances of the candidate inlier matches in circle blocks. The propagated criterion is calculated by affine matrix and circular feature of the inlier matches. The Simple Linear Iterative Clustering (SLIC) is presented to segment the host image into texture patches. Like pixel propagating, the circular block is slid in the corresponding texture blocks that the texture blocks contain the corresponding inlier matches to get more corresponding matches. Finally, some geometric image operations, such as dilation, are employed to eliminate the small holes or isolated pixels. A series of experiments showed that the proposed fusion algorithm can achieve superior performances than those of the moment invariant algorithms under various geometric transformations.

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“…The filtering stage is designed to remove those spurious pairs. In [48], a novel filtering algorithm was proposed which can effectively prune the falsely matched regions, besides their newly proposed interest point detector as above mentioned.…”

Section: Related Workmentioning

confidence: 99%

Copy-move forgery detection based on convolutional kernel network

Liu

Guan

Zhao

2017

Multimed Tools Appl

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In this paper, a copy-move forgery detection method based on Convolutional Kernel Network is proposed. Different from methods based on conventional hand-crafted features, Convolutional Kernel Network is a kind of data-driven local descriptor with the deep convolutional structure. Thanks to the development of deep learning theories and widely available datasets, the data-driven methods can achieve competitive performance on different conditions for its excellent discriminative capability. Besides, our Convolutional Kernel Network is reformulated as a series of matrix computations and convolutional operations which are easy to parallelize and accelerate by GPU, leading to high efficiency. Then, appropriate preprocessing and postprocessing for Convolutional Kernel Network are adopted to achieve copy-move forgery detection. Particularly, a segmentation-based keypoints distribution strategy is proposed and a GPU-based adaptive oversegmentation method is adopted. Numerous experiments are conducted to demonstrate the effectiveness and robustness of the GPU version of Convolutional Kernel Network, and the stateof-the-art performance of the proposed copy-move forgery detection method based on Convolutional Kernel Network.

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Iterative Copy-Move Forgery Detection Based on a New Interest Point Detector

Cited by 132 publications

References 40 publications

Verification of Web Videos Through Analysis of Their Online Context

Verification of Web Videos Through Analysis of Their Online Context

A Duplicated Forgery Detection Fusion Algorithm using SIFT and Radial-Harmonic Fourier Moments

Copy-move forgery detection based on convolutional kernel network

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