works on

From the 1 of 28 linked papers with an AI index.

activity
20242026
most citedFakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics

1 citations · 2 across the 10 of their papers we have counts for

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Showing 2025Show all

15 papers · 1 filter

cs.CL2025

UniHetero: Could Generation Enhance Understanding for Vision-Language-Model at Large Data Scale?

Fengjiao Chen, Minhao Jing, Weitao Lu +3

Vision-language large models are moving toward the unification of visual understanding and visual generation tasks. However, whether generation can enhance understanding is still u…

cs.SD2025

VoiceCloak: A Multi-Dimensional Defense Framework against Unauthorized Diffusion-based Voice Cloning

Qianyue Hu, Junyan Wu, Wei Lu +1

Diffusion Models (DMs) have achieved remarkable success in realistic voice cloning (VC), while they also increase the risk of malicious misuse. Existing proactive defenses designed…

cs.CV2025

Fine-grained Multiple Supervisory Network for Multi-modal Manipulation Detecting and Grounding

Xinquan Yu, Wei Lu, Xiangyang Luo

The task of Detecting and Grounding Multi-Modal Media Manipulation (DGM) is a branch of misinformation detection. Unlike traditional binary classification, it includes complex…

cs.CV2025

Weakly Supervised Multimodal Temporal Forgery Localization via Multitask Learning

Wenbo Xu, Wei Lu, Xiangyang Luo

The spread of Deepfake videos has caused a trust crisis and impaired social stability. Although numerous approaches have been proposed to address the challenges of Deepfake detecti…

cs.CV2025

A Multimodal Deviation Perceiving Framework for Weakly-Supervised Temporal Forgery Localization

Wenbo Xu, Junyan Wu, Wei Lu +2

Current researches on Deepfake forensics often treat detection as a classification task or temporal forgery localization problem, which are usually restrictive, time-consuming, and…

cs.CV2025

Weakly-Supervised Image Forgery Localization via Vision-Language Collaborative Reasoning Framework

Ziqi Sheng, Junyan Wu, Wei Lu +1

Image forgery localization aims to precisely identify tampered regions within images, but it commonly depends on costly pixel-level annotations. To alleviate this annotation burden…