A5092081627- Adversarial Robustness in Machine Learning
- Fault Detection and Control Systems
- Anomaly Detection Techniques and Applications
- Face and Expression Recognition
- Speech Recognition and Synthesis
- Network Security and Intrusion Detection
- Internet Traffic Analysis and Secure E-voting
- Neural Networks and Applications
- Machine Learning and ELM
Yancheng Institute of Technology
2024
Deep Neural Networks (DNNs) have been used to solve different day-to-day problems. Recently, DNNs deployed in real-time systems, and lowering the energy consumption response time has become need of hour. To address this scenario, researchers proposed incorporating dynamic mechanism static (SDNN) create Dynamic (DyNNs) performing amounts computation based on input complexity. Although into SDNNs would be preferable it also becomes important evaluate how introduction impacts robustness models....
Deep Learning (DL) models have been popular nowadays to execute different speech-related tasks, including automatic speech recognition (ASR). As ASR is being used in real-time scenarios, it important that the model remains efficient against minor perturbations input. Hence, evaluating efficiency robustness of need hour. We show like Speech2Text and Whisper dynamic computation based on inputs, causing efficiency. In this work, we propose SlothSpeech, a denial-of-service attack models, which...
Deep Neural Networks (DNNs) have been used to solve different day-to-day problems. Recently, DNNs deployed in real-time systems, and lowering the energy consumption response time has become need of hour. To address this scenario, researchers proposed incorporating dynamic mechanism static (SDNN) create Dynamic (DyNNs) performing amounts computation based on input complexity. Although into SDNNs would be preferable it also becomes important evaluate how introduction impacts robustness models....