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

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 detection, even if the watermarked image has been distorted. To recognize the selected object region after geometric distortions, 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 features of the distorted image are estimated and match them 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 receiver 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

Universal Image Steganalytic Method

In the paper we introduce a new universal steganalytic method in JPEG file format that is detecting well-known and also newly developed steganographic methods. The steganalytic model is trained by MHF-DZ steganographic algorithm previously designed by the same authors. The calibration technique with the Feature Based Steganalysis (FBS) was employed in order to identify statistical changes caused by embedding a secret data into original image. The steganalyzer concept utilizes Support Vector Machine (SVM) classification for training a model that is later used by the same steganalyzer in order to identify between a clean (cover) and steganographic image. The aim of the paper was to analyze the variety in accuracy of detection results (ACR) while detecting testing steganographic algorithms as F5, Outguess, Model Based Steganography without deblocking, JP Hide&Seek which represent the generally used steganographic tools. The comparison of four feature vectors with different lengths FBS (22), FBS (66) FBS(274) and FBS(285) shows promising results of proposed universal steganalytic method comparing to binary methods

Employing Psychoacoustic Model for Digital Audio Watermarking

This thesis discusses about digital audio watermarking by employing psychoacoustic model to make the watermarked signal inaudible to the audience. Due to the digital media data able to distribute easily without losing of data information, thus the intellectual property of musical creators and distributor may affected by this kind of circumstance . To prevent this, we propose the usage of spread spectrum technique and psychoacoustic model for embedding process, zero-forcing equalization and detection and wiener filtering for extracting process. Three samples of audio signal have been chosen for this experiment which are categorized as quiet, moderate, and noise state signal. The findings shows that our watermarking scheme achieved the intended purposes which are to test digital audio watermarking by employing psychoacoustic model, to embed different length of messages to test on accuracy of extracted data and to study the suitability on using hash function for verification of modification attacks