Iynkaran Natgunanathan

A Dual-Channel Time-Spread Echo Method for Audio Watermarking

This work proposes a novel dual-channel time-spread echo method for audio watermarking, aiming to improve robustness and perceptual quality. At the embedding stage, the host audio signal is divided into two subsignals, which are considered to be signals obtained from two virtual audio channels. The watermarks are implanted into the two subsignals simultaneously. Then the subsignals embedded with watermarks are combined to form the watermarked signal. At the decoding stage, the watermarked signal is split up into two watermarked subsignals. The similarity of the cepstra corresponding to the watermarked subsignals is exploited to extract the embedded watermarks. Moreover, if a properly designed colored pseudonoise sequence is used, the large peaks of its auto-correlation function can be utilized to further enhance the performance of watermark extraction. Compared with the existing time-spread echo-based schemes, the proposed method is more robust to attacks and has higher imperceptibility. The effectiveness of our method is demonstrated by simulation results.

Effective Pseudonoise Sequence and Decoding Function for Imperceptibility and Robustness Enhancement in Time-Spread Echo-Based Audio Watermarking

This paper proposes an effective pseudonoise (PN) sequence and the corresponding decoding function for time-spread echo-based audio watermarking. Different from the traditional PN sequence used in time-spread echo hiding, the proposed PN sequence has two features. Firstly, the echo kernel resulting from the new PN sequence has frequency characteristics with smaller magnitudes in perceptually significant region. This leads to higher perceptual quality. Secondly, the correlation function of the new PN sequence has three times more large peaks than that of the existing PN sequence. Based on this feature, we propose a new decoding function to improve the robustness of time-spread echo-based audio watermarking. The effectiveness of the proposed PN sequence and decoding function is illustrated by theoretical analysis, simulation examples, and listening test.

Robust Patchwork-Based Embedding and Decoding Scheme for Digital Audio Watermarking

This paper presents a novel patchwork-based embedding and decoding scheme for digital audio watermarking. At the embedding stage, an audio segment is divided into two subsegments and the discrete cosine transform (DCT) coefficients of the subsegments are computed. The DCT coefficients related to a specified frequency region are then partitioned into a number of frame pairs. The DCT frame pairs suitable for watermark embedding are chosen by a selection criterion and watermarks are embedded into the selected DCT frame pairs by modifying their coefficients, controlled by a secret key. The modifications are conducted in such a way that the selection criterion used at the embedding stage can be applied at the decoding stage to identify the watermarked DCT frame pairs. At the decoding stage, the secret key is utilized to extract watermarks from the watermarked DCT frame pairs. Compared with existing patchwork watermarking methods, the proposed scheme does not require information of which frame pairs of the watermarked audio signal enclose watermarks and is more robust to conventional attacks.

Protection of Big Data Privacy

In recent years, big data have become a hot research topic. The increasing amount of big data also increases the chance of breaching the privacy of individuals. Since big data require high computational power and large storage, distributed systems are used. As multiple parties are involved in these systems, the risk of privacy violation is increased. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. In particular, in this paper, we illustrate the infrastructure of big data and the state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. Furthermore, we discuss the challenges and future research directions related to privacy preservation in big data.

Robust Histogram Shape-Based Method for Image Watermarking

Cropping and random bending are two common attacks in image watermarking. In this paper we propose a novel image-watermarking method to deal with these attacks, as well as other common attacks. In the embedding process, we first preprocess the host image by a Gaussian low-pass filter. Then, a secret key is used to randomly select a number of gray levels and the histogram of the filtered image with respect to these selected gray levels is constructed. After that, a histogram-shape-related index is introduced to choose the pixel groups with the highest number of pixels and a safe band is built between the chosen and nonchosen pixel groups. A watermark-embedding scheme is proposed to insert watermarks into the chosen pixel groups. The usage of the histogram-shape-related index and safe band results in good robustness. Moreover, a novel high-frequency component modification mechanism is also utilized in the embedding scheme to further improve robustness. At the decoding end, based on the available secret key, the watermarked pixel groups are identified and watermarks are extracted from them. The effectiveness of the proposed image-watermarking method is demonstrated by simulation examples.

Patchwork-based audio watermarking method robust to de-synchronization attacks

This paper presents a patchwork-based audio watermarking method to resist de-synchronization attacks such as pitch-scaling, time-scaling, and jitter attacks. At the embedding stage, the watermarks are embedded into the host audio signal in the discrete cosine transform (DCT) domain. Then, a set of synchronization bits are implanted into the watermarked signal in the logarithmic DCT (LDCT) domain. At the decoding stage, we analyze the received audio signal in the LDCT domain to find the scaling factor imposed by an attack. Then, we modify the received signal to remove the scaling effect, together with the embedded synchronization bits. After that, watermarks are extracted from the modified signal. Simulation results show that at the embedding rate of 10 bps, the proposed method achieves 98.9% detection rate on average under the considered de-synchronization attacks. At the embedding rate of 16 bps, it can still obtain 94.7% detection rate on average. So, the proposed method is much more robust to de-synchronization attacks than other patchwork watermarking methods. Compared with the audio watermarking methods designed for tackling de-synchronization attacks, our method has much higher embedding capacity.

Spread spectrum-based high embedding capacity watermarking method for audio signals

Audio watermarking is a promising technology for copyright protection of audio data. Built upon the concept of spread spectrum (SS), many SS-based audio watermarking methods have been developed, where a pseudonoise (PN) sequence is usually used to introduce security. A major drawback of the existing SS-based audio watermarking methods is their low embedding capacity. In this paper, we propose a new SS-based audio watermarking method which possesses much higher embedding capacity while ensuring satisfactory imperceptibility and robustness. The high embedding capacity is achieved through a set of mechanisms: embedding multiple watermark bits in one audio segment, reducing host signal interference on watermark extraction, and adaptively adjusting PN sequence amplitude in watermark embedding based on the property of audio segments. The effectiveness of the proposed audio watermarking method is demonstrated by simulation examples.

Robust patchwork-based watermarking method for stereo audio signals

This paper presents a patchwork-based watermarking method for stereo audio signals, which exploits the similarity of the two sound channels of stereo signals. Given a segment of stereo signal, we first compute the discrete Fourier transforms (DFTs) of the two sound channels, which yields two sets of DFT coefficients. The DFT coefficients corresponding to certain frequency range are divided into multiple subsegment pairs and a criterion is proposed to select those suitable for watermark embedding. Then a watermark is embedded into the selected subsegment pairs by modifying their DFT coefficients. The exact way of modification is determined by a secret key, the watermark to be embedded, and the DFT coefficients themselves. In the decoding process, the subsegment pairs containing watermarks are identified by another criterion. Then the secret key is used to extract the watermark from the watermarked subsegments. Compared to the existing patchwork methods for audio watermarking, the proposed method does not require knowledge of which segments of the watermarked audio signal contain watermarks and is more robust to conventional attacks.

New directions in IoT privacy using attribute-based authentication

The Internet of Things (IoT) is a ubiquitous system that incorporates not only the current Internet of computers, but also smart objects and sensors. IoT technologies often rely on centralised architectures that follow the current business models. This makes efficient data collection and processing possible, which can be beneficial from a business perspective, but has many ramifications for users privacy. As communication within the IoT happens among many devices from various contexts, they need to authenticate each other to know that they talk to the intended party. Authentication, typically including identification, is the proof of identity information. However, transactions linked to the same identifier are traceable, and ultimately make people also traceable, hence their privacy is threatened. We propose a framework to counter this problem. We argue that applying attribute-based (AB) authentication in the context of IoT empowers users to maintain control over what data their devices disclose. At the same time AB authentication provides the possibility of data minimisation and unlinkability of user transactions. Therefore, this approach improves substantially user privacy in the IoT.

Histogram shape-based robust image watermarking method

Developing a watermarking method that is robust to cropping attack and random bending attacks (RBAs) is a challenging task in image watermarking. In this paper, we propose a histogram-based image watermarking method to tackle with both cropping attack and RBAs. In this method first the gray levels are divided into groups. Secondly the groups for watermark embedding are selected according to the number of pixels in them, which makes this method fully based on the histogram shape of the original image and adaptive to different images. Then the watermark bits are embedded by modifying the histogram of the selected groups. Since histogram shape is insensitive to cropping and independent from pixel positions, the proposed method is robust to cropping attack and RBAs. Besides, it also has high robustness against other common attacks. Experimental results demonstrate the effectiveness of the proposed method.