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A new approach for JPEG resize and image splicing detection

Authors:

Qingzhong Liu and

Andrew H. SungAuthors Info & Claims

MiFor '09: Proceedings of the First ACM workshop on Multimedia in forensics

October 2009

Pages 43 - 48

https://doi.org/10.1145/1631081.1631092

Published: 23 October 2009 Publication History

Abstract

Today's ubiquitous digital media are easily tampered by, e.g., removing or adding objects from or into images without leaving any obvious clues. JPEG is a most widely used standard in digital images and it can be easily doctored. It is therefore necessary to have reliable methods to detect forgery in JPEG images for applications in law enforcement, forensics, etc. In this paper, based on the correlation of neighboring Discrete Cosine Transform (DCT) coefficients, we propose a method to detect resized JPEG images and spliced images, which are widely used in image forgery. In detail, the neighboring joint density features of the DCT coefficients are extracted; then Support Vector Machines (SVM) are applied to the features for detection. To improve the evaluation of JPEG resized detection, we utilize the shape parameter of generalized Gaussian distribution (GGD) of DCT coefficients to measure the image complexity.

The study shows that our method is highly effective in detecting JPEG images resizing and splicing forgery. In the detection of resized JPEG image, the performance is related to both image complexity and resize scale factor. At the same scale factor, the detection performance in high image complexity is, as can be expected, lower than that in low image complexity.

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Cited By

Alencar ALopes MFernandes AAnjos JDe Paz Santana JLeithardt V(2024)Detection of Forged Images Using a Combination of Passive Methods Based on Neural NetworksFuture Internet10.3390/fi1603009716:3(97)Online publication date: 14-Mar-2024
https://doi.org/10.3390/fi16030097
Kumari RGarg H(2024)Image splicing forgery detection: A reviewMultimedia Tools and Applications10.1007/s11042-024-18801-zOnline publication date: 16-Mar-2024
https://doi.org/10.1007/s11042-024-18801-z
Gazzah SHaddada LShallal IAmara N(2023)Digital Image Forgery Detection with Focus on a Copy-Move Forgery Detection: A Survey2023 International Conference on Cyberworlds (CW)10.1109/CW58918.2023.00042(240-247)Online publication date: 3-Oct-2023
https://doi.org/10.1109/CW58918.2023.00042
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Index Terms

A new approach for JPEG resize and image splicing detection

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Information

Published In

MiFor '09: Proceedings of the First ACM workshop on Multimedia in forensics

October 2009

74 pages

ISBN:9781605587554

DOI:10.1145/1631081

General Chairs:
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Marcel Worring
University of Amsterdam, The Netherlands

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2009

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MM09: ACM Multimedia Conference

October 23, 2009

Beijing, China

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Cited By

Alencar ALopes MFernandes AAnjos JDe Paz Santana JLeithardt V(2024)Detection of Forged Images Using a Combination of Passive Methods Based on Neural NetworksFuture Internet10.3390/fi1603009716:3(97)Online publication date: 14-Mar-2024
https://doi.org/10.3390/fi16030097
Kumari RGarg H(2024)Image splicing forgery detection: A reviewMultimedia Tools and Applications10.1007/s11042-024-18801-zOnline publication date: 16-Mar-2024
https://doi.org/10.1007/s11042-024-18801-z
Gazzah SHaddada LShallal IAmara N(2023)Digital Image Forgery Detection with Focus on a Copy-Move Forgery Detection: A Survey2023 International Conference on Cyberworlds (CW)10.1109/CW58918.2023.00042(240-247)Online publication date: 3-Oct-2023
https://doi.org/10.1109/CW58918.2023.00042
Park JJohnson J(2023)RGB No More: Minimally-Decoded JPEG Vision Transformers2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.02139(22334-22346)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.02139
Das DNaskar RChakraborty R(2023)Image splicing detection with principal component analysis generated low-dimensional homogeneous feature set based on local binary pattern and support vector machineMultimedia Tools and Applications10.1007/s11042-023-14658-w82:17(25847-25864)Online publication date: 9-Mar-2023
https://doi.org/10.1007/s11042-023-14658-w
Das DNaskar R(2022)Image Splicing Detection based on Deep Convolutional Neural Network and Transfer Learning2022 IEEE 19th India Council International Conference (INDICON)10.1109/INDICON56171.2022.10039789(1-6)Online publication date: 24-Nov-2022
https://doi.org/10.1109/INDICON56171.2022.10039789
Sharma PKumar MSharma H(2022)Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluationMultimedia Tools and Applications10.1007/s11042-022-13808-w82:12(18117-18150)Online publication date: 1-Oct-2022
https://doi.org/10.1007/s11042-022-13808-w
Chennamma HMadhushree B(2022)A comprehensive survey on image authentication for tamper detection with localizationMultimedia Tools and Applications10.1007/s11042-022-13312-182:2(1873-1904)Online publication date: 14-Jun-2022
https://doi.org/10.1007/s11042-022-13312-1
Noor JSalim SIslam A(2021)Strategizing secured image storing and efficient image retrieval through a new cloud frameworkJournal of Network and Computer Applications10.1016/j.jnca.2021.103167192:COnline publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1016/j.jnca.2021.103167
Hayat KQazi T(2017)Forgery detection in digital images via discrete wavelet and discrete cosine transformsComputers and Electrical Engineering10.1016/j.compeleceng.2017.03.01362:C(448-458)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.compeleceng.2017.03.013
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