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Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts
2012
IEEE Transactions on Information Forensics and Security
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The proposed method is based on a new feature measuring the presence of demosaicing artifacts at a local level, and on a new statistical model allowing to derive the tampering probability of each 2 × 2 ...
We make the assumption that the image is acquired using a Color Filter Array, and that tampering removes the artifacts due to the demosaicing algorithm. ...
only a coarse grained localization of tampering is possible. ...
doi:10.1109/tifs.2012.2202227
fatcat:tt6zn75wvzgqxgoujp3kesfs4u
Content-Aware Detection Of Jpeg Grid Inconsistencies For Intuitive Image Forensics (Pre-Review)
2018
Zenodo
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An image segmentation step is introduced to differentiate between discontinuities produced by tampering and those that are attributed to image content, making the output maps easier to interpret by suppressing ...
The paper proposes a novel method for detecting indicators of image forgery by locating grid alignment abnormalities in JPEG compressed image bitmaps. ...
NOI1 [22] Models image noise using wavelet filtering and treats localized variances as possible forgeries. ...
doi:10.5281/zenodo.1246420
fatcat:k5kbirvwjvgwxguu66jmakz5vq
Detection of copy–move forgery using a method based on blur moment invariants
2007
Forensic Science International
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In this work we propose a method to automatically detect and localize duplicated regions in digital images. ...
The presence of duplicated regions in an image may signify a common type of forgery called copy-move forgery. ...
For example, image splicing in combination with copy-move forgery and localized image retouching techniques. ...
doi:10.1016/j.forsciint.2006.11.002
pmid:17161569
fatcat:bwg7nxcg7rdhtc5rm3puyppkbi
Multi-Clue Image Tampering Localization
2014
2014 IEEE International Workshop on Information Forensics and Security (WIFS)
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In this paper, we propose an algorithm for image tampering localization, based on the fusion of three separate detectors: i) one based on PRNU, working when we have at least a few of pictures shot with ...
Therefore, an effective strategy for tampering detection and localization requires to merge the output of many different forensic tools. ...
For this reason, the estimation of the binary tampering localization mask M ND requires thresholding the difference map and optionally processing it with some morphological operators. ...
doi:10.1109/wifs.2014.7084315
dblp:conf/wifs/GaboriniBMTT14
fatcat:2c6gjax7ovdx5jykzsjyshrsoa
Detection of Near-Duplicated Image Regions
[chapter]
2007
Advances in Soft Computing
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In this work we propose a method to automatically detect and localize near-duplicated regions in digital images. ...
The presence of nearduplicated regions in an image may signify a common type of forgery called copymove forgery. ...
In most cases of forgery investigated, they were able to detect duplicated regions in the tampered image despite of the presence of an acceptable amount of noise. ...
doi:10.1007/978-3-540-75175-5_24
dblp:series/asc/MahdianS08
fatcat:o3a3nqrcy5aqjgjcc4ffhyfmbq
An Improved Algorithm For Digital Image Authentication and Forgery Localization Using Demosaicing Artifacts
2019
IEEE Access
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This paper focuses on the digital image authentication and forgery localization using demosaicing artifacts. ...
Finally, a penalized expectation-maximization algorithm is used to localize forged areas in tampered images. ...
traces left by forgeries are exploited to distinguish between tampered and natural images. ...
doi:10.1109/access.2019.2938467
fatcat:3ja2dqfkejc27hc5utyetl7c2y
Robust Copy-Paste Detection Algorithm using SIFT for Digital Image Forensics
2019
International journal of recent technology and engineering
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region as well as creates inefficiency in the accuracy of forgeries. ...
The proposed approach can detect copy-paste forgeries effectively with high accuracy, reliability, and inconsistencies regardless of the test scenario. ...
Digital images via digital camera, scanner and computer graphics [2] , the integrity of information are the main requirement as it affects the judgment. ...
doi:10.35940/ijrte.d7841.118419
fatcat:titppm5tjvaf5p7pvlyngde25q
Fusion of Camera Model and Source Device Specific Forensic Methods for Improved Tamper Detection
[article]
2020
arXiv
pre-print
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For forgeries as small as 100×100 pixel size, the proposed method outperforms the state-of-the-art, which validates the usefulness of fusion for localization of small-size image forgeries. ...
These two methods also provide solutions to tamper localization problem. In this paper, we propose their combination via a Neural Network to achieve better small-scale tamper detection performance. ...
We also show that the CNN and NN models trained for the Fusion with specific camera models can be re-used for localization of forgeries on a tampered image captured with a new device from the same camera ...
arXiv:2002.10123v2
fatcat:uaoqxym4jrc55ensv4yeclw2ry
Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation
2019
EURASIP Journal on Image and Video Processing
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One type of forgery, known as copy-move duplication, is a specified type that usually involves image tampering. ...
Then, we refine these coarse forgery regions and remove mistakes or isolated areas. Finally, the forgery regions can be localized more precisely. ...
[8] used multiresolution local binary patterns (MLBP) for forgery detection. ...
doi:10.1186/s13640-019-0469-9
fatcat:q7n63zwrdjgdhmn5azmfhentpa
Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection
[article]
2022
arXiv
pre-print
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Depending on the content of the targeted images, media forgery could be divided into image tampering and Deepfake techniques. ...
Accordingly, the means of defense include image tampering detection and Deepfake detection, which share a wide variety of properties. ...
; 2) spatial forgery localization; 3) video forgery classification; 4) temporal forgery localization. ...
arXiv:2212.05667v1
fatcat:iuqs2d3qhbay3gryewjlbk75jm
Multi-spectral Class Center Network for Face Manipulation Detection and Localization
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2305.10794v1 fatcat:5vlmqrchgnfjxdy4qddznvrnum
Existing forgery localization methods suffer from imprecise and inconsistent pixel-level annotations. ...
To alleviate these problems, this paper first re-constructs the FaceForensics++ dataset by introducing pixel-level annotations, then builds an extensive benchmark for localizing tampered regions. ...
Image forensics tasks also aim to detect images as spoof or bona fide and locate the tampering regions, but most image forgery localization methods only focus on fake image datasets rather than real-fake ...
arXiv:2305.10794v2
fatcat:nrsw5vx4qbdknfv2rgi6j3fo7q
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
[article]
2021
arXiv
pre-print
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Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. ...
It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions. ...
They also achieve forgery localization by integrating imagelevel classification results using an overlapping stride of 8 pixels. ...
arXiv:2108.12947v1
fatcat:cscerhw2rreubob7tvos7jgvhq
Copy-Move Forgery Detection Based on Keypoint Clustering and Similar Neighborhood Search Algorithm
2020
IEEE Access
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Copy-move is one of the most commonly used methods of tampering with digital images. Keypoint-based detection is recognized as effective in copy-move forgery detection (CMFD). ...
This paper proposes an efficient CMFD method via clustering SIFT keypoints and searching the similar neighborhoods to locate tampered regions. ...
mark the image at pixel level and produce the final tampered localization map. ...
doi:10.1109/access.2020.2974804
fatcat:cv3f5ryrkrbanex3bkuwmefmey
Electromagnetismlike Mechanism Descriptor with Fourier Transform for a Passive Copy-move Forgery Detection in Digital Image Forensics
2017
Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
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Copy-move forgery is a special type of forgery that involves duplicating one region of an image by covering it with a copy of another region from the same image. ...
In the proposed algorithm, the image is divided into similar non-overlapping blocks, and then the final force for each block is evaluated and used to construct the tampered image features vector. ...
Tamper localization. The inverse of a procedure is conducted on the transformed image to retrieve the original image with a tampered region map. ...
doi:10.5220/0006232206120619
dblp:conf/icpram/DadkhahKJSMU17
fatcat:ev5qjoq22naxrle2mwjvutmfra
An Improved SIFT-Based Copy-Move Forgery Detection Method Using T-Linkage and Multi-Scale Analysis
2016
Journal of Information Hiding and Multimedia Signal Processing
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The detection of copy-move forgery is an important module in the image authentication. It aims to detect whether an image contains copy-move tampering and where the tampered regions are. ...
We make a final decision by considering all the localization maps with a voting strategy. ...
The final localization map will be obtained via a voting process. ...
dblp:journals/jihmsp/FanZL16
fatcat:cv227ydtivaf5dyqvt4wdhpzzm
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