← Search

Xinda Wang

4 accepted papers

2026

STYLE ATTACK DISGUISE: WHEN FONTS BECOME A CAMOUFLAGE FOR ADVERSARIAL INTENT

ICASSP 2026poster

With social media growth, users employ stylistic fonts and font-like emoji to express individuality, creating visually appealing text that remains human-readable. However, these fonts introduce hidden vulnerabilities in NLP models: while humans easily read stylistic text, models process these charac…

Cited by 0SourcePDFScholar
2026

TASE: Token Awareness and Structured Evaluation for Multilingual Language Models

AAAI 2026technical

While large language models (LLMs) have demonstrated remarkable performance on high-level semantic tasks, they often struggle with fine-grained, token-level understanding and structural reasoning—capabilities that are essential for applications requiring precision and control. We introduce TASE, a c

Cited by 0SourcePDFScholar
2025

PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models

EMNLP 2025

Prompt optimization is a practical and widely applicable alternative to fine tuning for improving large language model performance. Yet many existing methods evaluate candidate prompts by sampling full outputs, often coupled with self critique or human annotated preferences, which limits scalability

Cited by 0SourcePDFScholar
2025

Recoverable Facial Identity Protection via Adaptive Makeup Transfer Adversarial Attacks

AAAI 2025technical

Unauthorised face recognition (FR) systems have posed significant threats to digital identity and privacy protection. To alleviate the risk of compromised identities, recent makeup transfer-based attack methods embed adversarial signals in order to confuse unauthorised FR systems. However, their maj…