research-article

Exposing image forgery with blind noise estimation

Authors:

Xunyu Pan,

Xing Zhang,

Siwei LyuAuthors Info & Claims

MM&Sec '11: Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security

Pages 15 - 20

https://doi.org/10.1145/2037252.2037256

Published: 29 September 2011 Publication History

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Abstract

Noise is unwanted in high quality images, but it can aid image tampering. For example, noise can be intentionally added in image to conceal tampered regions and to create special visual effects. It may also be introduced unnoticed during camera imaging process, which makes the noise levels inconsistent in splicing images. In this paper, we propose a method to expose such image forgeries by detecting the noise variance differences between original and tampered parts of an image. The noise variance of local image blocks is estimated using a recently developed technique, where no prior information about the imaging device or original image is required. The tampered region is segmented from the original image by a two-phase coarse-to-fine clustering of image blocks. Our experimental results demonstrate that the proposed method can effectively detect image forgeries with high detection accuracy and low false positive rate both quantitatively and qualitatively.

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Cited By

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Capasso PCattaneo GDe Marsico M(2024)A Comprehensive Survey on Methods for Image IntegrityACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363320320:11(1-34)Online publication date: 12-Sep-2024
https://dl.acm.org/doi/10.1145/3633203
Zhang ZLi MLi XChang MHsieh J(2024)Image Manipulation Detection with Implicit Neural Representation and Limited SupervisionComputer Vision – ECCV 202410.1007/978-3-031-73223-2_15(255-273)Online publication date: 8-Nov-2024
https://doi.org/10.1007/978-3-031-73223-2_15
Malik DHasan AMihal MGupta K(2023)Recent Advancements in Image Forgery Detection Techniques: A Review2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)10.1109/ICCCIS60361.2023.10425617(794-799)Online publication date: 3-Nov-2023
https://doi.org/10.1109/ICCCIS60361.2023.10425617
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Index Terms

Exposing image forgery with blind noise estimation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
      1. Image processing

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Information

Published In

MM&Sec '11: Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security

September 2011

140 pages

ISBN:9781450308069

DOI:10.1145/2037252

General Chair:
Chad Heitzenrater
Air Force Research Lab, USA
,
Program Chairs:
Scott Craver
Binghamton University, USA
,
Jana Dittmann
Otto von Guericke University Magdeburg, Germany

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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Association for Computing Machinery

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Published: 29 September 2011

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SIGMM

MM&Sec '11: Multimedia and Security Workshop

September 29 - 30, 2011

New York, Buffalo, USA

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Overall Acceptance Rate 128 of 318 submissions, 40%

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Capasso PCattaneo GDe Marsico M(2024)A Comprehensive Survey on Methods for Image IntegrityACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363320320:11(1-34)Online publication date: 12-Sep-2024
https://dl.acm.org/doi/10.1145/3633203
Zhang ZLi MLi XChang MHsieh J(2024)Image Manipulation Detection with Implicit Neural Representation and Limited SupervisionComputer Vision – ECCV 202410.1007/978-3-031-73223-2_15(255-273)Online publication date: 8-Nov-2024
https://doi.org/10.1007/978-3-031-73223-2_15
Malik DHasan AMihal MGupta K(2023)Recent Advancements in Image Forgery Detection Techniques: A Review2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)10.1109/ICCCIS60361.2023.10425617(794-799)Online publication date: 3-Nov-2023
https://doi.org/10.1109/ICCCIS60361.2023.10425617
Yan CLi SLi H(2023) TransU 2 -Net: A Hybrid Transformer Architecture for Image Splicing Forgery Detection IEEE Access10.1109/ACCESS.2023.326401411(33313-33323)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3264014
Odabas Yildirim ETahaoglu GUlutas GUstubioglu BNabiyev V(2023)Color Image Splicing Localization Based on Block Classification Using Transition Probability MatrixWireless Personal Communications: An International Journal10.1007/s11277-023-10216-7129:3(1893-1919)Online publication date: 3-Mar-2023
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Liu KLi JHussain Bukhari S(2022)Overview of Image Inpainting and Forensic TechnologySecurity and Communication Networks10.1155/2022/92919712022Online publication date: 1-Jan-2022
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Li YWang C(2022)Image splicing tamper detection based on two-channel dilated convolution2022 3rd Asia Service Sciences and Software Engineering Conference10.1145/3523181.3523187(37-43)Online publication date: 24-Feb-2022
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Singh GGoyal P(2022)SDCN2: A Shallow Densely Connected CNN for Multi-Purpose Image Manipulation DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/351046218:3s(1-22)Online publication date: 31-Oct-2022
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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
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Pic MMahfoudi GTrabelsi ADugelay J(2022)Face Manipulation Detection in Remote Operational SystemsHandbook of Digital Face Manipulation and Detection10.1007/978-3-030-87664-7_19(413-436)Online publication date: 31-Jan-2022
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References

Cited By

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Recommendations

A bibliography of pixel-based blind image forgery detection techniques

Using noise inconsistencies for blind image forensics

Locating splicing forgery by adaptive-SVD noise estimation and vicinity noise descriptor

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