1,994 research outputs found
A Robust Fault-Tolerant and Scalable Cluster-wide Deduplication for Shared-Nothing Storage Systems
Deduplication has been largely employed in distributed storage systems to
improve space efficiency. Traditional deduplication research ignores the design
specifications of shared-nothing distributed storage systems such as no central
metadata bottleneck, scalability, and storage rebalancing. Further,
deduplication introduces transactional changes, which are prone to errors in
the event of a system failure, resulting in inconsistencies in data and
deduplication metadata. In this paper, we propose a robust, fault-tolerant and
scalable cluster-wide deduplication that can eliminate duplicate copies across
the cluster. We design a distributed deduplication metadata shard which
guarantees performance scalability while preserving the design constraints of
shared- nothing storage systems. The placement of chunks and deduplication
metadata is made cluster-wide based on the content fingerprint of chunks. To
ensure transactional consistency and garbage identification, we employ a
flag-based asynchronous consistency mechanism. We implement the proposed
deduplication on Ceph. The evaluation shows high disk-space savings with
minimal performance degradation as well as high robustness in the event of
sudden server failure.Comment: 6 Pages including reference
Bridging the Spoof Gap: A Unified Parallel Aggregation Network for Voice Presentation Attacks
Automatic Speaker Verification (ASV) systems are increasingly used in voice
bio-metrics for user authentication but are susceptible to logical and physical
spoofing attacks, posing security risks. Existing research mainly tackles
logical or physical attacks separately, leading to a gap in unified spoofing
detection. Moreover, when existing systems attempt to handle both types of
attacks, they often exhibit significant disparities in the Equal Error Rate
(EER). To bridge this gap, we present a Parallel Stacked Aggregation Network
that processes raw audio. Our approach employs a split-transform-aggregation
technique, dividing utterances into convolved representations, applying
transformations, and aggregating the results to identify logical (LA) and
physical (PA) spoofing attacks. Evaluation of the ASVspoof-2019 and VSDC
datasets shows the effectiveness of the proposed system. It outperforms
state-of-the-art solutions, displaying reduced EER disparities and superior
performance in detecting spoofing attacks. This highlights the proposed
method's generalizability and superiority. In a world increasingly reliant on
voice-based security, our unified spoofing detection system provides a robust
defense against a spectrum of voice spoofing attacks, safeguarding ASVs and
user data effectively
Securing Voice Biometrics: One-Shot Learning Approach for Audio Deepfake Detection
The Automatic Speaker Verification (ASV) system is vulnerable to fraudulent
activities using audio deepfakes, also known as logical-access voice spoofing
attacks. These deepfakes pose a concerning threat to voice biometrics due to
recent advancements in generative AI and speech synthesis technologies. While
several deep learning models for speech synthesis detection have been
developed, most of them show poor generalizability, especially when the attacks
have different statistical distributions from the ones seen. Therefore, this
paper presents Quick-SpoofNet, an approach for detecting both seen and unseen
synthetic attacks in the ASV system using one-shot learning and metric learning
techniques. By using the effective spectral feature set, the proposed method
extracts compact and representative temporal embeddings from the voice samples
and utilizes metric learning and triplet loss to assess the similarity index
and distinguish different embeddings. The system effectively clusters similar
speech embeddings, classifying bona fide speeches as the target class and
identifying other clusters as spoofing attacks. The proposed system is
evaluated using the ASVspoof 2019 logical access (LA) dataset and tested
against unseen deepfake attacks from the ASVspoof 2021 dataset. Additionally,
its generalization ability towards unseen bona fide speech is assessed using
speech data from the VSDC dataset
Ontology Evolution Using Recoverable SQL Logs
Logs of SQL queries are useful for building the system design, upgrading, and checking which SQL queries are running on certain applications. These SQL queries provide us useful information and knowledge about the system operations. The existing works use SQL query logs to find patterns when the underlying data and database schema is not available. For this purpose, a knowledge-base in the form of an ontology is created which is then mined for knowledge extraction. In this paper, we have proposed an approach to create and evolve an ontology from logs of SQL queries. Furthermore, when these SQL queries are transformed into the ontology, they loose their original form/shape i.e., we do not have original SQL queries. Therefore, we have further proposed a strategy to recover these SQL queries in their original form. Experiments on real world datasets demonstrate the effectiveness of the proposed approach
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