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Home/Authors/Ruiyang Qin

Ruiyang Qin

3 indexed papers

Recent (6 mo)
3
With code
0
Influential cites
0
Benchmarked
0

Publications per year

3
26

Top categories

AI×3Crypto×3Vision×3NLP×2ML×2Emerging Tech×1

Frequent co-authors

Dongrui Liu2×
Yu Li2×
Zhonghao Yang2×
Peng Wang2×
Guanxu Chen2×
Yuejin Xie2×

Research Timeline

2026
Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection

The paper proposes an end-to-end forensic pipeline using steganographic attribution and multimodal harm detection to reliably trace and attribute harmful misuse of AI-generated imagery on social platforms.

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex, open-world agentic scenarios.

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex open-world agent deployments.

Highlighted terms show continued research focus across papers

Papers

cs.AIcs.CLcs.CRRecentMay 28, 2026

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

Dongrui Liu, Yu Li, Zhonghao Yang, Peng Wang +46 more

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex, open-world agentic scenarios.

View →
cs.AIcs.CLcs.CRRecentMay 28, 2026

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

Dongrui Liu, Yu Li, Zhonghao Yang, Peng Wang +46 more

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex open-world agent deployments.

View →
cs.CVcs.AIcs.CRRecentApr 12, 2026

Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection

Xinlei Guan, David Arosemena, Tejaswi Dhandu, Kuan Huang +6 more

The paper proposes an end-to-end forensic pipeline using steganographic attribution and multimodal harm detection to reliably trace and attribute harmful misuse of AI-generated imagery on social platf…

View →