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Carsten Maple

3 papers indexed

arxivcs.CRcs.AI2026-07-07

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

Harsh Kasyap, Anil Kumar Pradhan, Ugur Ilker Atmaca, Graham Cormode, Carsten Maple

Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, maki…

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arxivcs.SDcs.AI2026-07-06

SynSFX: Multi-Model Sound Effects Synthesis Dataset for Deepfake Detection and Evaluation

Linxi Li, Yuncong Yu, Qianwei Guo, Liwei Jin, Yechen Wang, Carsten Maple

While audio deepfake detection has advanced significantly, representative detectors show limited generalization to synthetic sound effects. Existing environmental audio datasets such as EnvSDD provide important initial resources, but remain limited in scale and generation provena…

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arxivcs.SEcs.AI2026-07-04

Refused in Chat, Written in Code: Workflow-Level Jailbreak Construction in IDE Coding Agents

Abhishek Kumar, Carsten Maple

Large language models are increasingly deployed as IDE-integrated coding agents that decompose tasks, generate and edit files, run code, and refine outputs over many turns. Yet their safety is still often evaluated as if they were chatbots: one harmful prompt, one response, judge…

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