Localization of Copy-Move Forgery in speech signals through watermarking using DCT-QIM

Digital speech copyright protection and forgery identification are the prevalent issues in our advancing digital world. In speech forgery, voiced part of the speech signal is copied and pasted to a specific location which alters the meaning of the speech signal. Watermarking can be used to safe guard the copyrights of the owner. To detect copy-move forgeries a transform domain watermarking method is proposed. In the proposed method, watermarking is achieved through Discrete Cosine Transform (DCT) and Quantization Index Modulation (QIM) rule. Hash bits are also inserted in watermarked voice segments to detect Copy-Move Forgery (CMF) in speech signals. Proposed method is evaluated on two databases and achieved good imperceptibility. It exhibits robustness in detecting the watermark and forgeries against signal processing attacks such as resample, low-pass filtering, jittering, compression and cropping. The proposed work contributes for forensics analysis in speech signals. This proposed work also compared with the some of the state-of-art methods

Covert Channel for Improving VoIP Security

Abstract. In this paper a new way of exchanging data for Voice over Internet Protocol (VoIP) service is presented. With use of audio watermarking and network steganography techniques we achieve a covert channel which can be used for different purposes e.g. to improve IP Telephony signaling protocol's security or to alternate existing protocols like RTCP (Real-Time Control Protocol). In this paper we focus on improving VoIP security. The main advantage of this solution is that it is lightweight (it does not consume any transmission bandwidth) and the data sent is inseparably bound to the voice content

A Double Fragmentation Approach for Improving Virtual Primary Key-Based Watermark Synchronization

Relational data watermarking techniques using virtual primary key schemes try to avoid compromising watermark detection due to the deletion or replacement of the relation's primary key. Nevertheless, these techniques face the limitations that bring high redundancy of the generated set of virtual primary keys, which often compromises the quality of the embedded watermark. As a solution to this problem, this paper proposes double fragmentation of the watermark by using the existing redundancy in the set of virtual primary keys. This way, we guarantee the right identification of the watermark despite the deletion of any of the attributes of the relation. The experiments carried out to validate our proposal show an increment between 81.04% and 99.05% of detected marks with respect to previous solutions found in the literature. Furthermore, we found out that our approach takes advantage of the redundancy present in the set of virtual primary keys. Concerning the computational complexity of the solution, we performed a set of scalability tests that show the linear behavior of our approach with respect to the processes runtime and the number of tuples involved, making it feasible to use no matter the amount of data to be protected

Copyright protection of scalar and multimedia sensor network data using digital watermarking

This thesis records the research on watermarking techniques to address the issue of copyright protection of the scalar data in WSNs and image data in WMSNs, in order to ensure that the proprietary information remains safe between the sensor nodes in both. The first objective is to develop LKR watermarking technique for the copyright protection of scalar data in WSNs. The second objective is to develop GPKR watermarking technique for copyright protection of image data in WMSN

Watermark-based Sensor Data Authentication

Sensors have been widely used in robots, Internet of Things and automobiles. The data sent from sensor is used to detect objects, measure environment and make decision or conclusion. Since the sensor data is so critical for the system, the data from sensors must be authenticated before it is processed. The obvious approach is to use encryption. But this approach is not suitable for real-time streaming and may fail because of the noise or lossy compression. Besides, the receiver side must decrypt the data before displaying it and the encryption and decryption takes time when the data is huge, e.g. video streaming. In this paper, we propose an approach which combines encryption and watermarking to authenticate the sensor data. It has two phases and is spatial, invisible and blind-detected. We designed the approach carefully and try to achieve real-time performance. The experiment shows it is robust and fast.Master of Science in EngineeringComputer Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/148655/1/Master_Thesis__Zhe_Feng_(revised 2).pdfDescription of Master_Thesis__Zhe_Feng_(revised 2).pdf : Thesi

Parameterization of LSB in Self-Recovery Speech Watermarking Framework in Big Data Mining

The privacy is a major concern in big data mining approach. In this paper, we propose a novel self-recovery speech watermarking framework with consideration of trustable communication in big data mining. In the framework, the watermark is the compressed version of the original speech. The watermark is embedded into the least significant bit (LSB) layers. At the receiver end, the watermark is used to detect the tampered area and recover the tampered speech. To fit the complexity of the scenes in big data infrastructures, the LSB is treated as a parameter. This work discusses the relationship between LSB and other parameters in terms of explicit mathematical formulations. Once the LSB layer has been chosen, the best choices of other parameters are then deduced using the exclusive method. Additionally, we observed that six LSB layers are the limit for watermark embedding when the total bit layers equaled sixteen. Experimental results indicated that when the LSB layers changed from six to three, the imperceptibility of watermark increased, while the quality of the recovered signal decreased accordingly. This result was a trade-off and different LSB layers should be chosen according to different application conditions in big data infrastructures

Assessing the importance of audio/video synchronization for simultaneous translation of video sequences

Lip synchronization is considered a key parameter during interactive communication. In the case of video conferencing and television broadcasting, the differential delay between audio and video should remain below certain thresholds, as recommended by several standardization bodies. However, further research has also shown that these thresholds can be relaxed, depending on the targeted application and use case. In this article, we investigate the influence of lip sync on the ability to perform real-time language interpretation during video conferencing. Furthermore, we are also interested in determining proper lip sync visibility thresholds applicable to this use case. Therefore, we conducted a subjective experiment using expert interpreters, which were required to perform a simultaneous translation, and non-experts. Our results show that significant differences are obtained when conducting subjective experiments with expert interpreters. As interpreters are primarily focused on performing the simultaneous translation, lip sync detectability thresholds are higher compared with existing recommended thresholds. As such, primary focus and the targeted application and use case are important factors to be considered when selecting proper lip sync acceptability thresholds