Abstract
In this paper, we present techniques for steganalysis of images that have been potentially subjected to a watermarking algorithm. Our hypothesis is that a particular watermarking scheme leaves statistical evidence or structure that can be exploited for detection with the aid of proper selection of image features and multivariate regression analysis. We use some sophisticated image quality metrics as the feature set to distinguish between watermarked and unwatermarked images. To identify specific quality measures, which provide the best discriminative power, we use analysis of variance (ANOVA) techniques. The multivariate regression analysis is used on the selected quality metrics to build the optimal classifier using images and their blurred versions. The idea behind blurring is that the distance between an unwatermarked image and its blurred version is less than the distance between a watermarked image and its blurred version. Simulation results with a specific feature set and a well-known and commercially available watermarking technique indicates that our approach is able to accurately distinguish between watermarked and unwatermarked images.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Editors | P.W. Wong, E.J. Delp |
| Pages | 523-531 |
| Number of pages | 9 |
| Volume | 4314 |
| DOIs | |
| State | Published - 2001 |
| Event | Security and Watermarking of Multimedia Contents III - San Jose, CA, United States Duration: Jan 22 2001 → Jan 25 2001 |
Other
| Other | Security and Watermarking of Multimedia Contents III |
|---|---|
| Country | United States |
| City | San Jose, CA |
| Period | 1/22/01 → 1/25/01 |
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Keywords
- Image quality measures
- Multivariate regression analysis
- Steganalysis
- Watermarking
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics
Cite this
Steganalysis of watermarking techniques using image quality metrics. / Avcibaş, I.; Memon, N.; Sankur, B.
Proceedings of SPIE - The International Society for Optical Engineering. ed. / P.W. Wong; E.J. Delp. Vol. 4314 2001. p. 523-531.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Steganalysis of watermarking techniques using image quality metrics
AU - Avcibaş, I.
AU - Memon, N.
AU - Sankur, B.
PY - 2001
Y1 - 2001
N2 - In this paper, we present techniques for steganalysis of images that have been potentially subjected to a watermarking algorithm. Our hypothesis is that a particular watermarking scheme leaves statistical evidence or structure that can be exploited for detection with the aid of proper selection of image features and multivariate regression analysis. We use some sophisticated image quality metrics as the feature set to distinguish between watermarked and unwatermarked images. To identify specific quality measures, which provide the best discriminative power, we use analysis of variance (ANOVA) techniques. The multivariate regression analysis is used on the selected quality metrics to build the optimal classifier using images and their blurred versions. The idea behind blurring is that the distance between an unwatermarked image and its blurred version is less than the distance between a watermarked image and its blurred version. Simulation results with a specific feature set and a well-known and commercially available watermarking technique indicates that our approach is able to accurately distinguish between watermarked and unwatermarked images.
AB - In this paper, we present techniques for steganalysis of images that have been potentially subjected to a watermarking algorithm. Our hypothesis is that a particular watermarking scheme leaves statistical evidence or structure that can be exploited for detection with the aid of proper selection of image features and multivariate regression analysis. We use some sophisticated image quality metrics as the feature set to distinguish between watermarked and unwatermarked images. To identify specific quality measures, which provide the best discriminative power, we use analysis of variance (ANOVA) techniques. The multivariate regression analysis is used on the selected quality metrics to build the optimal classifier using images and their blurred versions. The idea behind blurring is that the distance between an unwatermarked image and its blurred version is less than the distance between a watermarked image and its blurred version. Simulation results with a specific feature set and a well-known and commercially available watermarking technique indicates that our approach is able to accurately distinguish between watermarked and unwatermarked images.
KW - Image quality measures
KW - Multivariate regression analysis
KW - Steganalysis
KW - Watermarking
UR - http://www.scopus.com/inward/record.url?scp=0034775171&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0034775171&partnerID=8YFLogxK
U2 - 10.1117/12.435436
DO - 10.1117/12.435436
M3 - Conference contribution
AN - SCOPUS:0034775171
VL - 4314
SP - 523
EP - 531
BT - Proceedings of SPIE - The International Society for Optical Engineering
A2 - Wong, P.W.
A2 - Delp, E.J.
ER -