Intelligent Systems in Latest DFA Compression Methods for DPC

K, Vinoth Kumar; Prithi, S.; Vinodha, K

doi:10.4018/978-1-7998-9795-8.ch009

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…First, an autoencoder (AE) is trained to compress bits of raw audio into an easier representation to process and to invert this representation into an audio signal (i. e., vocoder). For instance, mel-spectrogram representations were often used (e. g., [28]) before being replaced by more recent socalled neural codecs -as Soundstream [32], Encodec [33] or DAC [34] -that demonstrated better reconstructions. For interested readers, the latter commonly employ discretised latent spaces as codebooks of tokens -e. g., Residual Vector Quantization (RVQ) [35].…”

Section: Proposed Frameworkmentioning

confidence: 99%

Of Spiky SVDs and Music Recommendation

Afchar,

Hennequin,

Guigue

2023

Proceedings of the 17th ACM Conference on Recommender Systems

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In the face of a new era of generative models, the detection of artificially generated content has become a matter of utmost importance. The ability to create credible minute-long music deepfakes in a few seconds on userfriendly platforms poses a real threat of fraud on streaming services and unfair competition to human artists. This paper demonstrates the possibility (and surprising ease) of training classifiers on datasets comprising real audio and fake reconstructions, achieving a convincing accuracy of 99.8%. To our knowledge, this marks the first publication of a music deepfake detector, a tool that will help in the regulation of music forgery. Nevertheless, informed by decades of literature on forgery detection in other fields, we stress that a good test score is not the end of the story. We step back from the straightforward ML framework and expose many facets that could be problematic with such a deployed detector: calibration, robustness to audio manipulation, generalisation to unseen models, interpretability and possibility for recourse. This second part acts as a position for future research steps in the field and a caveat to a flourishing market of fake content checkers.

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Section: Proposed Frameworkmentioning

confidence: 99%

Of Spiky SVDs and Music Recommendation

Afchar,

Hennequin,

Guigue

2023

Proceedings of the 17th ACM Conference on Recommender Systems

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“…Since DFA has the fantastical time complexity of O(1), a variety of studies [14]- [20] concentrate on trying to reduce the memory usage of DFA to support large-scale regex rulesets. These studies have found that the state transition table of DFA is sparse and propose various compression methods accordingly.…”

Section: A Software Rem Schemesmentioning

confidence: 99%

FPGA-CPU Architecture Accelerated Regular Expression Matching With Fast Preprocessing

Zhong

Chen

Han

2022

The Computer Journal

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Regular Expression Matching (REM) is the core of Deep Packet Inspection (DPI), which is important for various network security applications. The burgeoning Software Defined Network and Network Function Virtualization technologies make the network evolve more dynamic, which brings serious challenges for DPI engines to achieve high matching performance with fast rule-set update capability. To meet these challenges, this paper proposes a heterogeneous Field Programmable Gate Array (FPGA)-Central Processing Unit (CPU) architecture to accelerate Deterministic Finite Automaton (DFA)-based REM with high preprocessing performance. Firstly, a novel regex decomposition technique is proposed to solve the DFA state explosion problem, which splits each regex into one prefix and several postfixes. Secondly, heterogeneous architecture is presented to collaboratively handle regex matching, in which prefixes are matched in parallel in an FPGA and postfixes are matched in a CPU. To further improve the matching performance, several well-designed DFA compression techniques and regex decomposition optimizations are proposed. Our design has been implemented in a DPI prototype employing a medium-end FPGA. Extensive experiments are conducted to evaluate the performance. Results reveal that our proposed architecture achieves 6.33 Gbps matching throughput on the Snort rule-set (v3.0), which is close to state-of-the-art FPGA NFA-based schemes. However, the rule-set preprocessing time is significantly reduced to <7 minutes, compared with up to several hours of FPGA NFA-based countermeasures.

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Intelligent Systems in Latest DFA Compression Methods for DPC

Cited by 2 publications

References 8 publications

Of Spiky SVDs and Music Recommendation

Of Spiky SVDs and Music Recommendation

FPGA-CPU Architecture Accelerated Regular Expression Matching With Fast Preprocessing

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