2 citations · 4 across the 6 of their papers we have counts for
9 papers
ScenePercept: Scenario-Conditioned Perceptual Assessment of Real-World User-Generated Content Images
Shuhan Xia, Yu Chen, Xiang Wang +3
The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic and comprehensive perceptual asse…
MM-VeriAgent: Learning to Use Extensive Tools to Verify Multimodal Misinformation with Reinforcement Learning
Peipei Li, Shuhan Xia, Shengyang Liu +2
Real-world multimodal misinformation often involves mixed forgery sources, requiring sample-specific detection strategies. Existing tool-augmented methods rely on predefined workfl…
3-Tracer: A Tri-level Temporal-Aware Framework for Audio Forgery Detection and Localization
Shuhan Xia, Xuannan Liu, Xing Cui +1
Recently, partial audio forgery has emerged as a new form of audio manipulation. Attackers selectively modify partial but semantically critical frames while preserving the overall…
CAHS-Attack: CLIP-Aware Heuristic Search Attack Method for Stable Diffusion
Shuhan Xia, Jing Dai, Hui Ouyang +3
Diffusion models exhibit notable fragility when faced with adversarial prompts, and strengthening attack capabilities is crucial for uncovering such vulnerabilities and building mo…
AVFakeBench: A Comprehensive Audio-Video Forgery Detection Benchmark for AV-LMMs
Shuhan Xia, Peipei Li, Xuannan Liu +3
The threat of Audio-Video (AV) forgery is rapidly evolving beyond human-centric deepfakes to include more diverse manipulations across complex natural scenes. However, existing ben…
SpineBench: Benchmarking Multimodal LLMs for Spinal Pathology Analysis
Chenghanyu Zhang, Zekun Li, Peipei Li +5
With the increasing integration of Multimodal Large Language Models (MLLMs) into the medical field, comprehensive evaluation of their performance in various medical domains becomes…
Video-SafetyBench: A Benchmark for Safety Evaluation of Video LVLMs
Xuannan Liu, Zekun Li, Zheqi He +6
The increasing deployment of Large Vision-Language Models (LVLMs) raises safety concerns under potential malicious inputs. However, existing multimodal safety evaluations primarily…
Jailbreak Attacks and Defenses against Multimodal Generative Models: A Survey
Xuannan Liu, Xing Cui, Peipei Li +6
The rapid evolution of multimodal foundation models has led to significant advancements in cross-modal understanding and generation across diverse modalities, including text, image…
MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs
Xuannan Liu, Zekun Li, Peipei Li +6
Current multimodal misinformation detection (MMD) methods often assume a single source and type of forgery for each sample, which is insufficient for real-world scenarios where mul…