The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Contrast Enhancement-Based Forensics in Digital Images
Open accessAbstract
As a retouching manipulation, contrast enhancement is typically used to adjust the global brightness and contrast of digital images. Malicious users may also perform contrast enhancement locally for creating a realistic composite image. As such it is significant to detect contrast enhancement blindly for verifying the originality and authenticity of the digital images. In this paper, we propose two novel algorithms to detect the contrast enhancement involved manipulations in digital images. First, we focus on the detection of global contrast enhancement applied to the previously JPEG-compressed images, which are widespread in real applications. The histogram peak/gap artifacts incurred by the JPEG compression and pixel value mappings are analyzed theoretically, and distinguished by identifying the zero-height gap fingerprints. Second, we propose to identify the composite image created by enforcing contrast adjustment on either one or both source regions. The positions of detected blockwise peak/gap bins are clustered for recognizing the contrast enhancement mappings applied to different source regions. The consistency between regional artifacts is checked for discovering the image forgeries and locating the composition boundary. Extensive experiments have verified the effectiveness and efficacy of the proposed techniques.
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Cites
JPEG: Still Image Data Compression Standard
Pennebaker, W. B., & Mitchell, J. L. (1992) · Medical Entomology and Zoology · Cited by 2,390
JPEG: Still image data compression standard
Sebestyen, I. (1993) · Computer Standards & Interfaces · doi:10.1016/0920-5489(93)90038-s · Cited by 1,768
”Break Our Steganographic System”: The Ins and Outs of Organizing BOSS
Bas, P., Filler, T., & Pevný, T. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24178-9_5 · Cited by 1,250
Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution
Huang, S.-C., Cheng, F.-C., & Chiu, Y.-S. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2226047 · Cited by 1,233
UCID: an uncompressed color image database:
Schaefer, G., & Stich, M. (2003) · SPIE Proceedings · doi:10.1117/12.525375 · Cited by 1,071
A Histogram Modification Framework and Its Application for Image Contrast Enhancement
Arici, T., Dikbas, S., & Altunbasak, Y. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2021548 · Cited by 943
Farid, H. (2009) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.931079 · Cited by 925
Exposing digital forgeries by detecting traces of resampling
Popescu, A. C., & Farid, H. (2005) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2004.839932 · Cited by 795
Exposing digital forgeries by detecting traces of resampling
Popescu, A., & Farid, H. (2005) · doi:10.1109/tsp.2004.839932(410 · Cited by 655
Forensic detection of image manipulation using statistical intrinsic fingerprints
Stamm, M. C., & Liu, K. J. R. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2053202 · Cited by 321
Cited by
Fighting Fake News: Two Stream Network for Deepfake Detection via Learnable SRM
Han, B., Han, X., Zhang, H., Li, J., & Cao, X. (2021) · IEEE Transactions on Biometrics, Behavior, and Identity Science · doi:10.1109/tbiom.2021.3065735 · Cited by 77
Anti-forensics of median filtering and contrast enhancement
Sharma, S., Ravi, H., Subramanyam, A. V., & Emmanuel, S. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.102682 · Cited by 14
Metadata
- Authors
- Cao, G., Zhao, Y., Ni, R., & Li, X.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2014-03
- Volume
- 9
- Issue
- 3
- Pages
- 515-525
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2009437133
- Cited by
- 258
- References
- 23
- FWCI
- 22.55
- Type
- journal article
- Language
- en
- Open access
- green
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- Cao, G., Zhao, Y., Ni, R., & Li, X. (2014). Contrast Enhancement-Based Forensics in Digital Images. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2014.2300937
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