6 papers · 1 filter
REVEAL: Reference-Grounded Reasoning for Multimodal Manipulation Detection
Jun Zhou, Bingwen Hu, Yaxiong Wang +4
Multimodal manipulation detection aims to simultaneously identify forged image--text pairs and localize tampered regions, yet existing methods typically rely on memorizing isolated…
Where Concept Erasure Should Occur: Concept-Layer Alignment in Text-to-Video Diffusion Models
Yiwei Xie, Ping Liu, Zheng Zhang
Text-to-video diffusion transformers encode semantic information unevenly across model depth, which constrains effective concept erasure. We identify a representational bottleneck,…
Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models
Bincheng Peng, Guang Li, Ping Liu +2
Dataset distillation compresses a large training set into a small synthetic set that preserves downstream training utility. While most existing methods target training networks fro…
PROBE: Diagnosing Residual Concept Capacity in Erased Text-to-Video Diffusion Models
Yiwei Xie, Zheng Zhang, Ping Liu
Concept erasure techniques for text-to-video (T2V) diffusion models report substantial suppression of sensitive content, yet current evaluation is limited to checking whether the t…
Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges
Ping Liu, Qiqi Tao, Joey Tianyi Zhou
As synthetic media, including video, audio, and text, become increasingly indistinguishable from real content, the risks of misinformation, identity fraud, and social manipulation…
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator
Xin Zhang, Jiawei Du, Ping Liu +1
Dataset distillation has emerged as a technique aiming to condense informative features from large, natural datasets into a compact and synthetic form. While recent advancements ha…