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Exposing image forgery with blind noise estimation

Published: 29 September 2011 Publication History

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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  • (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
  • (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
  • (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
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cover image ACM Conferences
MM&Sec '11: Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
September 2011
140 pages
ISBN:9781450308069
DOI:10.1145/2037252
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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Publication History

Published: 29 September 2011

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Author Tags

  1. image forensics
  2. noise estimation
  3. unsupervised learning

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MM&Sec '11
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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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Cited By

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  • (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
  • (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
  • (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
  • (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
  • (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
  • (2022)Overview of Image Inpainting and Forensic TechnologySecurity and Communication Networks10.1155/2022/92919712022Online publication date: 1-Jan-2022
  • (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
  • (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
  • (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
  • (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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