Block-level data deduplication is a widely-used technology for saving storage space by filtering the data blocks with the same hash value. However, existing block-level data deduplication approaches either ignore the data consistency of deduplication or suffer severe performance degradation for providing consistency guarantees. In this paper, we propose Consistency-Aware Deduplication (CADedup+) to achieve high-performance block-level data deduplication with data consistency. The main idea of CADedup+ is to achieve an efficient journaling mechanism for deduplication by taking advantage of persistent memory (PM), such as byte-addressability and near-DRAM access latency. To balance the trade-offs between performance and consistency requirements in data deduplication, we carefully design three modes of journaling mechanism, i.e., writeback mode, ordered mode, and journal mode, for CADedup+. We properly place the deduplication metadata of CADedup+ onto the DRAM-PM hybrid memory architecture to minimize PM costs according to the features of metadata updates. The deduplication metadata on PM is managed by a set of metadata transactions and updated with the help of the efficient hardware atomic operations provided by CPU. We implement CADedup+ in the generic block layer in Linux kernel 4.9.0. We conduct extensive experiments on Intel Optane PMEM to evaluate CADedup+ with typical benchmarks. Experimental results show that CADedup+ can reduce 63%-70% write volume and 50%-60% I/O latency over Dmdedup, a widely-used open-source block-level data deduplication system, while ensuring deduplication consistency.

Block-level data deduplication is prevalent in various-scaled storage systems for saving storage space and improving I/O performance by reducing write operations. However, data deduplication induces additional metadata of blocks, leading to I/O amplification. Furthermore, to ensure the correctness of deduplicated user data, data deduplication systems need to guarantee crash consistency. In this paper, we propose CADedup, to achieve high performance while ensuring crash consistency by using persistent memory. By taking advantage of the byte-addressability and near-DRAM latency of persistent memory, we design an efficient journaling mechanism to manage the deduplication metadata of CADedup. Additionally, we adopt a hybrid storage architecture of DRAM and persistent memory to minimize space costs. We implement CADedup through the device-mapper interface in the Linux kernel. We conduct extensive experiments on Intel Optane PMEM to evaluate CAD-edup with widely-used benchmarks. Experimental results show that compared with the no-deduplication system, CADedup can achieve up to 1×-3× improvement in many workloads of server storage and has negligible throughput drop in the worst case.

Digital watermarking is one of the most widely used techniques for protection of ownership rights of digital audio, images and video. Its commercial applications range from copyright protection to digital rights management. The success of a digital watermarking technology depends heavily on its robustness to withstand attacks that are aimed at removing or destroying the watermark from its host data. This paper provides analysis of a number of digital image watermark attacks and attempts to classify them into categories. A set of experimental results are also provided to show the effect of these attacks on watermarks produced using different watermarking techniques.