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This work details our approach to achieving a leading system with a 1.79% pooled equal error rate (EER) on the evaluation set of the Controlled Singing Voice Deepfake Detection (CtrSVDD). The rapid advancement of generative AI models…

Irregular embedding lookups are a critical bottleneck in recommender models, sparse large language models, and graph learning models. In this paper, we first demonstrate that, by offloading these lookups to specialized access units,…

Fully homomorphic encryption (FHE) allows computations over encrypted data. This technique makes privacy-preserving cloud computing a reality. Users can send their encrypted sensitive data to a cloud server, get encrypted results returned…

Fully Homomorphic Encryption (FHE) enables privacy-preserving computation and has many applications. However, its practical implementation faces massive computation and memory overheads. To address this bottleneck, several…

Foundation model reliability assessment typically requires thousands of evaluation examples, making it computationally expensive and time-consuming for real-world deployment. We introduce microprobe, a novel approach that achieves…

While Secure Aggregation (SA) protects update confidentiality in Cross-silo Federated Learning, it fails to guarantee aggregation integrity, allowing malicious servers to silently omit or tamper with updates. Existing verifiable aggregation…

Automated analysis of electroencephalography (EEG) has recently undergone a paradigm shift. The introduction of transformer architectures and self-supervised pretraining (SSL) has led to the development of EEG foundation models. These…

Spiking Transformers have recently emerged as promising architectures for combining the efficiency of spiking neural networks with the representational power of self-attention. However, the lack of standardized implementations, evaluation…

Edge deployment of transformer-based models increasingly relies on ASIC accelerators due to their high performance and energy efficiency, achieved through optimized dataflows, specialized architectures, low-bitwidth computation, and…

Motivation: The pan-genome can be stored as elastic-degenerate (ED) string, a recently introduced compact representation of multiple overlapping sequences. However, a search over the ED string does not indicate which individuals (if any)…

In the age of Big Data, releasing protected sensitive data at a future point in time is critical for various applications. Such self-emerging data release requires the data to be protected until a prescribed data release time and be…

This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well as target speakers' spoofed inputs should be addressed.…

Frontier AI models pose increasing risks to public safety and international security, creating a pressing need for AI developers to provide credible guarantees about their development activities without compromising proprietary information.…

With the proliferation of high-speed wireless networking, the necessity for efficient, robust and secure encryption modes is ever increasing. But, cryptography is primarily a computationally intensive process. This paper investigates the…

Solidity is an object-oriented and high-level language for writing smart contracts that are used to execute, verify and enforce credible transactions on permissionless blockchains. In the last few years, analysis of smart contracts has…

In cryptographic engineering, extensive attention has been devoted to ameliorating the performance and security of the algorithms within. Nonetheless, in the state-of-the-art, the approaches for increasing the reliability of the efficient…

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of…

Poorly designed smart contracts are particularly vulnerable, as they may allow attackers to exploit weaknesses and steal the virtual currency they manage. In this study, we train a model using unsupervised learning to identify…

In this paper, we present end-to-end and speech embedding based systems trained in a self-supervised fashion to participate in the ACM Multimedia 2022 ComParE Challenge, specifically the stuttering sub-challenge. In particular, we exploit…

The proliferation of autonomous AI agents marks a paradigm shift toward complex, emergent multi-agent systems. This transition introduces systemic security risks, including control-flow hijacking and cascading failures, that traditional…