Search Results for author:
Found 8 papers, 1 papers with code
SOPHON: Non-Fine-Tunable Learning to Restrain Task Transferability For Pre-trained Models
In addition, we carefully design the optimization process to entrap the pre-trained model within a hard-to-escape local optimum regarding restricted domains.
SafeGen: Mitigating Unsafe Content Generation in Text-to-Image Models
The key idea is to eliminate unsafe visual representations from the model regardless of the text input.
Catch You and I Can: Revealing Source Voiceprint Against Voice Conversion
Voice conversion (VC) techniques can be abused by malicious parties to transform their audios to sound like a target speaker, making it hard for a human being or a speaker verification/identification system to trace the source speaker.
V-Cloak: Intelligibility-, Naturalness- & Timbre-Preserving Real-Time Voice Anonymization
Apart from the anonymity loss, we further incorporate the intelligibility loss and the psychoacoustics-based naturalness loss.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar
Specifically, we propose a novel \textit{Frequency Component Detection} method to detect the existence of mmWave signals, distinguish between mmWave radar and WiGig signals using a waveform classifier based on a convolutional neural network (CNN), and localize spy radars using triangulation based on the detector's observations at multiple anchor points.
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning
Recent works have revealed that backdoor attacks against Deep Reinforcement Learning (DRL) could lead to abnormal action selection of the agent, which may result in failure or even catastrophe in crucial decision processes.
FakeWake: Understanding and Mitigating Fake Wake-up Words of Voice Assistants
In the area of Internet of Things (IoT) voice assistants have become an important interface to operate smart speakers, smartphones, and even automobiles.
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants
With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc.
