Tootfinder

HMTRace: Hardware-Assisted Memory-Tagging based Dynamic Data Race Detection
Jaidev Shastri, Xiaoguang Wang, Basavesh Ammanaghatta Shivakumar, Freek Verbeek, Binoy Ravindran
https://arxiv.org/abs/2404.19139

HMTRace: Hardware-Assisted Memory-Tagging based Dynamic Data Race Detection
Data race, a category of insidious software concurrency bugs, is often challenging and resource-intensive to detect and debug. Existing dynamic race detection tools incur significant execution time and memory overhead while exhibiting high false positives. This paper proposes HMTRace, a novel Armv8.5-A memory tag extension (MTE) based dynamic data race detection framework, emphasizing low compute and memory requirements while maintaining high accuracy and precision. HMTRace supports race detect…

AI-assisted Tagging of Deepfake Audio Calls using Challenge-Response
Govind Mittal, Arthur Jakobsson, Kelly O. Marshall, Chinmay Hegde, Nasir Memon
https://arxiv.org/abs/2402.18085

AI-assisted Tagging of Deepfake Audio Calls using Challenge-Response
Scammers are aggressively leveraging AI voice-cloning technology for social engineering attacks, a situation significantly worsened by the advent of audio Real-time Deepfakes (RTDFs). RTDFs can clone a target's voice in real-time over phone calls, making these interactions highly interactive and thus far more convincing. Our research confidently addresses the gap in the existing literature on deepfake detection, which has largely been ineffective against RTDF threats. We introduce a robust chal…