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research-article

Exposing photo manipulation with inconsistent reflections

Published: 02 February 2012 Publication History

Abstract

The advent of sophisticated photo editing software has made it increasingly easier to manipulate digital images. Often visual inspection cannot definitively distinguish the resulting forgeries from authentic photographs. In response, forensic techniques have emerged to detect geometric or statistical inconsistencies that result from specific forms of photo manipulation. In this article we describe a new forensic technique that focuses on geometric inconsistencies that arise when fake reflections are inserted into a photograph or when a photograph containing reflections is manipulated. This analysis employs basic rules of reflective geometry and linear perspective projection, makes minimal assumptions about the scene geometry, and only requires the user to identify corresponding points on an object and its reflection. The analysis is also insensitive to common image editing operations such as resampling, color manipulations, and lossy compression. We demonstrate this technique with both visually plausible forgeries of our own creation and commercially produced forgeries.

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Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 31, Issue 1
January 2012
149 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/2077341
Issue’s Table of Contents
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

New York, NY, United States

Publication History

Published: 02 February 2012
Accepted: 01 July 2011
Revised: 01 July 2011
Received: 01 April 2011
Published in TOG Volume 31, Issue 1

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Author Tags

  1. Reflections
  2. center of projection
  3. forgery detection
  4. image forensics
  5. image manipulation
  6. mirrors
  7. photo manipulation

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Cited By

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  • (2024)An Efficient Image Forgery Detection Framework using Transfer Learning Models2024 International Telecommunications Conference (ITC-Egypt)10.1109/ITC-Egypt61547.2024.10620518(507-512)Online publication date: 22-Jul-2024
  • (2024)AI-Generated Image Detection With Wasserstein Distance Compression and Dynamic Aggregation2024 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP51287.2024.10648186(3827-3833)Online publication date: 27-Oct-2024
  • (2024)A Review of Deepfake Techniques: Architecture, Detection, and DatasetsIEEE Access10.1109/ACCESS.2024.347725712(154718-154742)Online publication date: 2024
  • (2023)Face Forgery Detection by 3D Decomposition and Composition SearchIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.3233586(1-16)Online publication date: 2023
  • (2023)Towards Universal Fake Image Detectors that Generalize Across Generative Models2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.02345(24480-24489)Online publication date: Jun-2023
  • (2023)Integrating TPS, cylindrical projection, and plumb-line constraint for natural stitching of multiple imagesThe Visual Computer10.1007/s00371-023-03065-940:5(3795-3824)Online publication date: 31-Aug-2023
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  • (2022)Physical IntegrityMultimedia Forensics10.1007/978-981-16-7621-5_9(207-234)Online publication date: 2-Apr-2022
  • (2021)Face Forgery Detection by 3D Decomposition2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.00295(2928-2938)Online publication date: Jun-2021
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