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Shouling Ji

6 papers hereh-index 574 citations12 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4
  • last author2

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CR4
  • cs.CL2
same name
  • Shouling Ji — 75 papers, h 53
  • Shouling Ji — 28 papers, h 6
  • Shouling Ji — 19 papers, h 12
  • Shouling Ji — 11 papers, h 3
  • Shouling Ji — 6 papers, h 4
  • Shouling Ji — 6 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedWatch the Watcher! Backdoor Attacks on Security-Enhancing Diffusion Models

1 citations · 2 across the 6 of their papers we have counts for

collaborators
Showing cs.CRShow all

4 papers · 1 filter

cs.CR2025

NeuroBreak: Unveil Internal Jailbreak Mechanisms in Large Language Models

Chuhan Zhang, Ye Zhang, Bowen Shi +5

Jailbreak attacks bypass the safety alignment of large language models (LLMs) to elicit harmful outputs, yet the vast parameter space makes diagnosing the underlying failure mechan…

cs.CR2024

Watermark under Fire: A Robustness Evaluation of LLM Watermarking

Jiacheng Liang, Zian Wang, Lauren Hong +2

Various watermarking methods (``watermarkers'') have been proposed to identify LLM-generated texts; yet, due to the lack of unified evaluation platforms, many critical questions re…

cs.CR2024★ 1 cited

Watch the Watcher! Backdoor Attacks on Security-Enhancing Diffusion Models

Changjiang Li, Ren Pang, Bochuan Cao +4

Thanks to their remarkable denoising capabilities, diffusion models are increasingly being employed as defensive tools to reinforce the security of other models, notably in purifyi…

cs.CR2023★ 1 cited

On the Difficulty of Defending Contrastive Learning against Backdoor Attacks

Changjiang Li, Ren Pang, Bochuan Cao +4

Recent studies have shown that contrastive learning, like supervised learning, is highly vulnerable to backdoor attacks wherein malicious functions are injected into target models,…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.