activity
20212026
most citedHufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networks

4 citations · 6 across the 9 of their papers we have counts for

collaborators

11 papers

cs.LG2026

Differentially Private Natural Gradient Descent

Pan Li, Kai Chen, Shuai Chang +3

Under a fixed privacy budget, the utility of differentially private (DP) training is ultimately determined by its optimization efficiency. Standard first-order DP optimizers such a…

cs.CR2026

Securing Code Understanding: Detecting Natural Backdoor Vulnerability in Code Language Models

Yuchen Chen, Weisong Sun, Haocheng Huang +11

Code Language Models (CodeLMs) have become integral to software engineering, significantly advancing code intelligence tasks. However, their widespread adoption has raised critical…

cs.AI2026

Execution-Grounded Security Testing for Coding Agents in Software Engineering Pipelines

Yifei Ge, Weisong Sun, Jinkun Xiao +8

Coding agents are increasingly integrated into system operations, where their tool use can directly modify project artifacts, execution environments, and the underlying system. For…

cs.SE2026

PuzzleMark: Implicit Jigsaw Learning for Robust Code Dataset Watermarking in Neural Code Completion Models

Haocheng Huang, Yuchen Chen, Weisong Sun +5

Constructing and curating high-quality code datasets requires significant resources, making them valuable intellectual property. Unfortunately, these datasets currently face severe…

cs.CR2025

LoRAGuard: An Effective Black-box Watermarking Approach for LoRAs

Peizhuo Lv, Yiran Xiahou, Congyi Li +4

LoRA (Low-Rank Adaptation) has achieved remarkable success in the parameter-efficient fine-tuning of large models. The trained LoRA matrix can be integrated with the base model thr…

cs.CR2025

RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models

Peizhuo Lv, Mengjie Sun, Hao Wang +5

In recent years, tremendous success has been witnessed in Retrieval-Augmented Generation (RAG), widely used to enhance Large Language Models (LLMs) in domain-specific, knowledge-in…