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Physics-motivated features for distinguishing photographic images and computer graphics

Published: 06 November 2005 Publication History

Abstract

The increasing photorealism for computer graphics has made computer graphics a convincing form of image forgery. Therefore, classifying photographic images and photorealistic computer graphics has become an important problem for image forgery detection. In this paper, we propose a new geometry-based image model, motivated by the physical image generation process, to tackle the above-mentioned problem. The proposed model reveals certain physical differences between the two image categories, such as the gamma correction in photographic images and the sharp structures in computer graphics. For the problem of image forgery detection, we propose two levels of image authenticity definition, i.e., imaging-process authenticity and scene authenticity, and analyze our technique against these definitions. Such definition is important for making the concept of image authenticity computable. Apart from offering physical insights, our technique with a classification accuracy of 83.5% outperforms those in the prior work, i.e., wavelet features at 80.3% and cartoon features at 71.0%. We also consider a recapturing attack scenario and propose a counter-attack measure. In addition, we constructed a publicly available benchmark dataset with images of diverse content and computer graphics of high photorealism.

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cover image ACM Conferences
MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia
November 2005
1110 pages
ISBN:1595930442
DOI:10.1145/1101149
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: 06 November 2005

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Author Tags

  1. computer graphics
  2. differential geometry
  3. fractal
  4. image authentication
  5. image forensics
  6. natural image statistics
  7. steganalysis

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MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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  • (2024)Detect with Style: A Contrastive Learning Framework for Detecting Computer-Generated ImagesInformation10.3390/info1503015815:3(158)Online publication date: 11-Mar-2024
  • (2024)Simple Methods for Improving the Forensic Classification between Computer-Graphics Images and Natural ImagesForensic Sciences10.3390/forensicsci40100104:1(164-183)Online publication date: 14-Mar-2024
  • (2024)Neural Network-Based Algorithm for Identification of Recaptured ImagesInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142350036238:01Online publication date: 29-Jan-2024
  • (2024)CGFormer: ViT-Based Network for Identifying Computer-Generated Images With Token LabelingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332208319(235-250)Online publication date: 2024
  • (2024)Computer-Generated Image Detection Based on Multi-Scale Feature Fusion Attention Module2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)10.1109/ICCECT60629.2024.10545903(273-277)Online publication date: 26-Apr-2024
  • (2024)Color Patterns And Enhanced Texture Learning For Detecting Computer-Generated ImagesThe Computer Journal10.1093/comjnl/bxae00767:6(2303-2316)Online publication date: 7-Mar-2024
  • (2024)Detecting Artificial Intelligence-Generated images via deep trace representations and interactive feature fusionInformation Fusion10.1016/j.inffus.2024.102578112(102578)Online publication date: Dec-2024
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  • (2023)Self-Supervised Learning for the Distinction between Computer-Graphics Images and Natural ImagesApplied Sciences10.3390/app1303188713:3(1887)Online publication date: 1-Feb-2023
  • (2023)Image Splicing Detection Using Retinex Based Contrast Enhancement and Deep Learning2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)10.1109/ICACCTech61146.2023.00127(771-778)Online publication date: 23-Dec-2023
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