1 citations · 1 across the 4 of their papers we have counts for
7 papers
RiSE: Residual Subspace Expert for Generalizable Text-Centric Image Forgery Localization
Kahim Wong, Kemou Li, Yiming Chen +2
As AI-assisted image editing becomes increasingly prevalent, Text-Centric Image Forgery Localization (TFL) is essential for protecting trust in financial and legal records. However…
LLM Persona Unlearning
Kemou Li, Zhuan Shi, Qizhou Wang +4
Pre-training equips large language models (LLMs) with a broad repertoire of behavioral patterns associated with roles, styles, values, and goals. Post-training teaches conditional…
Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing
Kemou Li, Qizhou Wang, Yue Wang +6
Open-weight LLMs are released not only as fixed products but also as substrates for downstream fine-tuning. This openness, however, creates legal and ethical risks because users ma…
CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review
Jicheng Zhou, Kemou Li, Kahim Wong +5
Recent reports during the AAAI-27 review cycle highlight the risk of reviewers coordinating bids for reciprocal assignment advantage. Prior work treats bidding, reviewer assignment…
When Modalities Fail to Tango: Conformal Backdoor Detection in Multimodal Contrastive Learning
Yiming Chen, Kemou Li, Haiwei Wu +1
Backdoor attacks in multimodal contrastive learning (MCL) have garnered growing attention in recent years, as many downstream tasks critically depend on pre-trained MCL models. Exi…
SEED: Simple ViT and Evolving Harness for Explainable Text Forgery Detection
Kahim Wong, Kemou Li, Yiming Chen +2
AI-assisted image editing threatens trust in financial, legal, and identity records. The GenText-Forensics Challenge at ACM MM 2026 addresses this by requiring structured forensic…
Regroup Median Loss for Combating Label Noise
Fengpeng Li, Kemou Li, Jinyu Tian +1
The deep model training procedure requires large-scale datasets of annotated data. Due to the difficulty of annotating a large number of samples, label noise caused by incorrect an…