JOURNAL ARTICLE

Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain

Manuel Cedillo-HernándezFrancisco García-UgaldeMariko Nakano-MiyatakeHéctor Pérez-Meana

Year: 2013 Journal:   Brno University of Technology Digital Library (Brno University of Technology)   Publisher: Brno University of Technology

Abstract

In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT) magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF) algorithm to allow the correct watermark detec- tion, even if the watermarked image has been distorted. To recognize the selected object region after geometric dis- tortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF fea- tures of the distorted image are estimated and matched with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and the Visual Information Fidelity (VIF). The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The re- ceiver operating characteristics (ROC) curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided.

Keywords:
Digital watermarking Feature (linguistics) Matching (statistics) Computer science Domain (mathematical analysis) Artificial intelligence Pattern recognition (psychology) Object (grammar) Computer vision Feature matching Mathematics Feature extraction Image (mathematics) Statistics

Metrics

17
Cited By
1.30
FWCI (Field Weighted Citation Impact)
20
Refs
0.85
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Steganography and Watermarking Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Digital Media Forensic Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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