The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
How realistic is photorealistic?
Abstract
Computer graphics rendering software is capable of generating highly photorealistic images that can be impossible to differentiate from photographic images. As a result, the unique stature of photographs as a definitive recording of events is being diminished (the ease with which digital images can be manipulated is, of course, also contributing to this demise). To this end, we describe a method for differentiating between photorealistic and photographic images. Specifically, we show that a statistical model based on first-order and higher order wavelet statistics reveals subtle but significant differences between photorealistic and photographic images.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
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Libsvm : A library for support vector machines
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Pattern Classification and Scene Analysis
Thompson, M., Duda, R. O., & Hart, P. E. (1974) · Leonardo · doi:10.2307/1573081 · Cited by 4,495
Indoor-outdoor image classification
Szummer, M., & Picard, R. W. (2002) · Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database · doi:10.1109/caivd.1998.646032 · Cited by 639
Image compression via joint statistical characterization in the wavelet domain
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Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
Lyu, S., & Farid, H. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-36415-3_22 · Cited by 450
Self-citationA theory of multirate filter banks
Vetterli, M. (1987) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1987.1165137 · Cited by 410
ON IMAGE CLASSIFICATION: CITY IMAGES VS. LANDSCAPES
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Steganalysis using color wavelet statistics and one-class support vector machines
Lyu, S., & Farid, H. (2004) · SPIE Proceedings · doi:10.1117/12.526012 · Cited by 168
Self-citation
Cited by
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979
Noiseprint: A CNN-Based Camera Model Fingerprint
Cozzolino, D., & Verdoliva, L. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2916364 · Cited by 499
Detection of GAN-Generated Fake Images over Social Networks
Marra, F., Gragnaniello, D., Cozzolino, D., & Verdoliva, L. (2018) · 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00084 · Cited by 399
Information Forensics: An Overview of the First Decade
Stamm, M. C., Wu, M., & Liu, K. J. R. (2013) · IEEE Access · doi:10.1109/access.2013.2260814 · Cited by 394
Metadata
- Authors
- Lyu, S., & Farid, H.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2005-02
- Volume
- 53
- Issue
- 2
- Pages
- 845-850
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W4244098250
- Cited by
- 280
- References
- 12
- FWCI
- 14.36
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
