Hybrid Image-Retrieval Method for Image-Splicing Validation

Pham, Nam; Lee, Jong Weon; Kwon, Goo‐Rak; Park, Chun-Su

doi:10.3390/sym11010083

Cited by 44 publications

(26 citation statements)

References 53 publications

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“…precision (P p ), recall (R p ) and F 1 score ( F p ). These pixel-level metrics are beneficial for evaluating the general localization performance of the algorithm [38]. At pixel-level, the precision is defined as the ratio of the number of correctly detected forged pixels to the number of totally detected forged pixels and recall is defined as the ratio of the number of correctly detected forged pixels to the number of forged pixels in the ground-truth forged image.…”

Section: Localization Resultsmentioning

confidence: 99%

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Kaur¹,

Jindal²,

Singh³

2021

Turk J Elec Eng & Comp Sci

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Digital passive image forgery methods are extensively used to verify the authenticity and integrity of images. Splicing and copy-move are the most common types of passive digital image forgeries. Several approaches have been proposed to detect these forgeries separately, but very few approaches are available that can detect them simultaneously. However, a more efficient method is still in demand to meet the day-today challenges to detect these forgeries at the same time. So, a passive hybrid approach based on discrete fractional cosine transform (DFrCT) and local binary pattern (LBP) is proposed to detect copy-move and splicing forgeries simultaneously. The extra parameter i.e. fractional parameter of DFrCT is utilized to enhance the accuracy and LBP is used to highlight the tampering artifacts effectively. Then, a support vector machine (SVM) is employed to categorize the images into authentic, copy-move, and spliced images. Next, localization is performed on both the copy-move and spliced images to localize the duplicated areas in the image. Experiments on six benchmark datasets, namely, CASIA v1.0, GRIP, CASIA v2.0, IMD, COVERAGE, and Columbia, attain accuracy rates of 99.67%, 99.23%, 99.76%, 98.81%, 95%, and 98.17%, respectively. To validate the effectiveness of the proposed method, comparative analysis has been performed with existing methods in terms of ROC, precision, recall, F1 score, F2 score, and accuracy. Moreover, the robustness of the proposed work is tested under rotation attack and better results are attained in comparison to the existing techniques.

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Section: Localization Resultsmentioning

confidence: 99%

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Kaur¹,

Jindal²,

Singh³

2021

Turk J Elec Eng & Comp Sci

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“…Their main purpose is to train smart systems (machines) through Machine/Deep learning techniques (ML/DL), but without adapting to the needs of human visual examination. Specifically, images from datasets, recommended for the associated forensic tools, were initially regarded, such as “the-wild-web-tampered-image-dataset” 8 ( Zampoglou et al., 2016 ), the CASIA 9 ( Dong et al., 2013 ) and CASIA v2.0 10 datasets ( Pham et al., 2019 ), the “Image Manipulation Dataset” 11 ( Christlein et al., 2012 ) and the “Deutsche Welle Image Forensics Dataset” 12 ( Zampoglou et al., 2016 ). However, in most of these repositories, numerous examples seem to be irrelevant as news-reporting documents (i.e., they cannot be used to document specific news-stories, which was the main focus of the current work).…”

Section: Methodsmentioning

confidence: 99%

News authentication and tampered images: evaluating the photo-truth impact through image verification algorithms

Katsaounidou¹,

Gardikiotis²,

Tsipas³

et al. 2020

Heliyon

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Photos have been used as evident material in news reporting almost since the beginning of Journalism. In this context, manipulated or tampered pictures are very common as part of informing articles, in today's misinformation crisis. The current paper investigates the ability of people to distinguish real from fake images. The presented data derive from two studies. Firstly, an online cross-sectional survey (N = 120) was conducted to analyze ordinary human skills in recognizing forgery attacks. The target was to evaluate individuals' perception in identifying manipulated visual content, therefore, to investigate the feasibility of “crowdsourced validation”. This last term refers to the process of gathering fact-checking feedback from multiple users, thus collaborating towards assembling pieces of evidence on an event. Secondly, given that contemporary veracity solutions are coupled with both journalistic principles and technology developments, an experiment in two phases was employed: a) A repeated measures experiment was conducted to quantify the associated abilities of Media and Image Experts (N = 5 + 5) in detecting tampering artifacts. In this latter case, image verification algorithms were put into the core of the analysis procedure to examine their impact on the authenticity assessment task. b) Apart from conducting interview sessions with the selected experts and their proper guidance in using the tools, a second experiment was also deployed on a larger scale through an online survey (N = 301), aiming at validating some of the initial findings. The primary intent of the deployed analysis and their combined interpretation was to evaluate image forensic services, offered as real-world tools, regarding their comprehension and utilization by ordinary people, involved in the everyday battle against misinformation. The outcomes confirmed the suspicion that only a few subjects had prior knowledge of the implicated algorithmic solutions. Although these assistive tools often lead to controversial or even contradictory conclusions, their experimental treatment with the systematic training in their proper use boosted the participants' performance. Overall, the research findings indicate that the scores of successful detections, relying exclusively on human observations, cannot be disregarded. Hence, the ultimate challenge for the “verification industry” should be to balance between forensic automations and the human experience, aiming at defending the audience from inaccurate information propagation.

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“…We experiment with two approaches for our use caseone with using only the tampered images and it's corresponding mask [17] and the other in which we incorporate a mask for each authentic image i.e. we create an empty mask for an authentic image and train our model using the combined dataset.…”

Section: Basic Structure Of a Unet Architecturementioning

confidence: 99%

“…For the purposes of this paper, we will use the CA-SIA2.0 dataset [7] introduced by Jing Dong et al with its corresponding ground truth masks [17] by Nam Thanh et al The dataset contains 7408 authentic images and 5123 tampered images out of which 3295 are of copy-move type and 1828 are spliced images. Each tampered image has a corresponding binary mask indicating the area of tamper.…”

Section: Datasetmentioning

confidence: 99%

Detecting and Localizing Copy-Move and Image-Splicing Forgery

Pandey¹,

Mitra²

2022

Preprint

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In the world of fake news and deepfakes, there have been an alarmingly large number of cases of images being tampered with and published in newspapers, used in court and posted on social media for defamation purposes.Detecting these tampered images is an important task and one we try to tackle. In this paper, we focus on the methods to detect if an image has been tampered with using both Deep Learning and Image transformation methods and comparing the performances and robustness of each method. We then attempt to identify the tampered area of the image and predict the corresponding mask. Based on the results, suggestions and approaches are provided to achieve a more robust framework to detect and identify the forgeries.

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Hybrid Image-Retrieval Method for Image-Splicing Validation

Cited by 44 publications

References 53 publications

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

News authentication and tampered images: evaluating the photo-truth impact through image verification algorithms

Detecting and Localizing Copy-Move and Image-Splicing Forgery

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