arXiv subjects

Xuanbing Wen

Publications and source records attributed to Xuanbing Wen.

2 recordsLinked to original sources

arXiv2026

Detecting Object Hallucinations in Large Vision-Language Models via Cross-Modal Attention Drifts and Mask-Based Verification

Despite recent advances in large vision-language models (LVLMs), object hallucination remains a major barrier to their reliable deployment. Existing detection methods often characterize visual grounding using attention from individual layers, leaving its evolution across layers underexplored. We propose CADMP, a lightweight object hallucination detection framework that combines adjacent-layer cross-modal attention drift with prediction sensitivity to targeted visual masking. During decoding, CADMP quantifies distributional changes between consecutive cross-modal attention maps to capture abrupt transitions in visual grounding. It then selects the transition with the largest drift, locates the corresponding visually relevant regions, and measures the change in prediction probability after masking these regions. These two signals provide complementary evidence: attention drift characterizes the stability of internal visual grounding, while probability variation verifies whether a prediction truly depends on the identified visual evidence. A lightweight detector integrates both signals to identify hallucinated predictions. Experiments on multiple benchmarks and representative open-source LVLMs demonstrate that CADMP achieves consistently competitive detection performance. Ablation studies further confirm the complementary contributions of adjacent-layer drift modeling and mask-based grounding verification.

cs.CV↗

arXiv2025

LADSG: Label-Anonymized Distillation and Similar Gradient Substitution for Label Privacy in Vertical Federated Learning

Vertical Federated Learning (VFL) has emerged as a promising paradigm for collaborative model training across distributed feature spaces, which enables privacy-preserving learning without sharing raw data. However, recent studies have confirmed the feasibility of label inference attacks by internal adversaries. By strategically exploiting gradient vectors and semantic embeddings, attackers-through passive, active, or direct attacks-can accurately reconstruct private labels, leading to catastrophic data leakage. Existing defenses, which typically address isolated leakage vectors or are designed for specific types of attacks, remain vulnerable to emerging hybrid attacks that exploit multiple pathways simultaneously. To bridge this gap, we propose Label-Anonymized Defense with Substitution Gradient (LADSG), a unified and lightweight defense framework for VFL. LADSG first anonymizes true labels via soft distillation to reduce semantic exposure, then generates semantically-aligned substitute gradients to disrupt gradient-based leakage, and finally filters anomalous updates through gradient norm detection. It is scalable and compatible with standard VFL pipelines. Extensive experiments on six real-world datasets show that LADSG reduces the success rates of all three types of label inference attacks by 30-60% with minimal computational overhead, demonstrating its practical effectiveness.

cs.CR↗