Digital Design for Image-Adaptive Watermarking using CDF 5/3 Wavelet

Paper This paper presents a hardware architecture for image-adaptive watermarking in the wavelet domain. The embedding strength factor is selected by calculating the energy present between the different frequency bands. The current algorithm is constructed on a CDF 5/3 wavelet based on the model of lossless compression JPEG 2000. Wavelet filters are implemented using a parallel architecture with a lifting scheme, which makes them more efficient in terms of speed and hardware utilization. The top module of the system is built with the combination of serial-parallel architecture to balance the speed and power consumption. The presented watermarking system is tested using hardware in the loop-testing technique. The objective is to develop an image-adaptive, real time, low power consumption and robust watermarking system, which can be incorporated into existing hardware such as digital cameras, scanners, and camcorders. The watermarking system's efficiency against different assaults has been evaluated using the StirMark software. The proposed watermarking system showed robustness against most of the geometric and non-geometric attacks.

SLA-Based Adaptation Schemes in Distributed Stream Processing Engines

With the upswing in the volume of data, information online, and magnanimous cloud applications, big data analytics becomes mainstream in the research communities in the industry as well as in the scholarly world. This prompted the emergence and development of real-time distributed stream processing frameworks, such as Flink, Storm, Spark, and Samza. These frameworks endorse complex queries on streaming data to be distributed across multiple worker nodes in a cluster. Few of these stream processing frameworks provides fundamental support for controlling the latency and throughput of the system as well as the correctness of the results. However, none has the ability to handle them on the fly at runtime. We present a well-informed and efficient adaptive watermarking and dynamic buffering timeout mechanism for the distributed streaming frameworks. It is designed to increase the overall throughput of the system by making the watermarks adaptive towards the stream of incoming workload, and scale the buffering timeout dynamically for each task tracker on the fly while maintaining the Service Level Agreement (SLA)-based end-to-end latency of the system. This work focuses on tuning the parameters of the system (such as window correctness, buffering timeout, and so on) based on the prediction of incoming workloads and assesses whether a given workload will breach an SLA using output metrics including latency, throughput, and correctness of both intermediate and final results. We used Apache Flink as our testbed distributed processing engine for this work. However, the proposed mechanism can be applied to other streaming frameworks as well. Our results on the testbed model indicate that the proposed system outperforms the status quo of stream processing. With the inclusion of learning models like naïve Bayes, multilayer perceptron (MLP), and sequential minimal optimization (SMO)., the system shows more progress in terms of keeping the SLA intact as well as quality of service (QoS).

Blind Image Watermarking Based on Adaptive Data Spreading in n-Level DWT Subbands

This paper proposes a new adaptive watermarking scheme for digital images, which has the properties of blind extraction, invisibility, and robustness against attacks. The typical scheme for invisibility and robustness consisted of two main techniques: finding local positions to be watermarked and mixing or embedding the watermark into the pixels of the locations. In finding the location, however, our scheme uses a global space such that the multiple watermarking data is spread out over all four lowest-frequency subbands, resulting from n-level Mallat-tree 2D (dimensional) DWT, where n depends on the amount of watermarking data and the resolution of the host image, without any further process to find the watermarking locations. To embed the watermark data into the subband coefficients, weighting factors are used according to the type and energy of each subband to adjust the strength of the watermark, so we call this an adaptive scheme. To examine the ability of the proposed scheme, images with various resolutions are tested for various attacks, both pixel-value changing attacks and geometric attacks. With experimental results and comparison to the existing works we show that the proposed scheme has better performance than the previous works, except those which specialize in certain types of attacks.