Search Results for author:
Found 7 papers, 3 papers with code
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation
Vision Language Models (VLMs) have shown remarkable performance, but are also vulnerable to backdoor attacks whereby the adversary can manipulate the model's outputs through hidden triggers.
AMQA: An Adversarial Dataset for Benchmarking Bias of LLMs in Medicine and Healthcare
Large language models (LLMs) are reaching expert-level accuracy on medical diagnosis questions, yet their mistakes and the biases behind them pose life-critical risks.
Prompt Injection attack against LLM-integrated Applications
We deploy HouYi on 36 actual LLM-integrated applications and discern 31 applications susceptible to prompt injection.
FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software
The effectiveness and efficiency of these systems heavily depend on the quality of the training datasets.
Deep Learning for Android Malware Defenses: a Systematic Literature Review
In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment.
Hero: On the Chaos When PATH Meets Modules
The results showed that \textsc{Hero} achieved a high detection rate of 98. 5\% on a DM issue benchmark and found 2, 422 new DM issues in 2, 356 popular Golang projects.
Software Engineering
Testing Deep Learning Models for Image Analysis Using Object-Relevant Metamorphic Relations
The corresponding rate for the object detection models is over 8. 5%.
