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cs.CR2026
The More It Says, the More You Pay: A Black-Box Audit of Provider-Side Token Inflation in LLM Services
Leilei Chen, Lan Zhang, Chen Tang +5
In pay-per-token LLM services, the more a model says, the more users pay. Dishonest providers can covertly manipulate generation to inflate output tokens while largely preserving t…
cs.CR2025
Untraceable DeepFakes via Traceable Fingerprint Elimination
Jiewei Lai, Lan Zhang, Chen Tang +3
Recent advancements in DeepFakes attribution technologies have significantly enhanced forensic capabilities, enabling the extraction of traces left by generative models (GMs) in im…
cs.CR2025
DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models
Qihao Lin, Chen Tang, Lan zhang +2
As large language models (LLMs) grow more powerful, concerns over copyright infringement of LLM-generated texts have intensified. LLM watermarking has been proposed to trace unauth…