Search Results for author:
Found 14 papers, 8 papers with code
A Proactive and Dual Prevention Mechanism against Illegal Song Covers empowered by Singing Voice Conversion
To improve the imperceptibility of perturbations, we refine a psychoacoustic model-based loss with the backing track as an additional masker, a unique accompanying element for singing voices compared to ordinary speech voices.
Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Study
To bridge these gaps, we conduct a large-scale study on the latent vulnerable functions in two commonly used SV datasets and their utilization for function-level and line-level SV predictions.
When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference
These findings motivate our exploration of dynamic inference in code completion and inspire us to enhance it with a decision-making mechanism that stops the generation of incorrect code.
Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?
The correctness and unambiguity of API usage among these code models are crucial for achieving desirable program functionalities, requiring them to learn various API fully qualified names structurally and semantically.
CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models
Even worse, the ``black-box'' nature of neural models sets a high barrier for externals to audit their training datasets, which further connives these unauthorized usages.
Source Code Data Augmentation for Deep Learning: A Survey
This paper fills this gap by conducting a comprehensive and integrative survey of data augmentation for source code, wherein we systematically compile and encapsulate existing literature to provide a comprehensive overview of the field.
Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems
The experimental results show that the proposed estimator helps save 23. 3% of computational cost measured in floating-point operations for the code completion systems, and 80. 2% of rejected prompts lead to unhelpful completion
On the Importance of Building High-quality Training Datasets for Neural Code Search
The performance of neural code search is significantly influenced by the quality of the training data from which the neural models are derived.
CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning
Github Copilot, trained on billions of lines of public code, has recently become the buzzword in the computer science research and practice community.
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty
Based on this, we propose an automated testing technique to generate multiple types of uncommon AEs and BEs that are largely missed by existing techniques.
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
In this paper, we conduct the first comprehensive and systematic study of the adversarial attacks on SR systems (SRSs) to understand their security weakness in the practical blackbox setting.
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
Vulnerability identification is crucial to protect the software systems from attacks for cyber security.
LEOPARD: Identifying Vulnerable Code for Vulnerability Assessment through Program Metrics
The latter needs the prior knowledge of known vulnerabilities and can only identify similar but not new types of vulnerabilities.
Software Engineering
DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems
Our in-depth evaluation on a state-of-the-art speech-to-text DL system demonstrates the effectiveness of our technique in improving quality and reliability of stateful DL systems.
