26 citations · 35 across the 13 of their papers we have counts for
13 papers
RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild
Danni Xu, Shaojing Fan, Harry Cheng +1
Multimodal misinformation increasingly leverages visual persuasion, where repurposed or manipulated images strengthen misleading text. We introduce \textbf{RW-Post}, a post-aligned…
Detecting Deepfakes via Hamiltonian Dynamics
Harry Cheng, Ming-Hui Liu, Tianyi Wang +3
Driven by the rapid development of generative AI models, deepfake detectors are compelled to undergo periodic recalibration to capture newly developed synthetic artifacts. To break…
Towards Generalizable Deepfake Detection via Real Distribution Bias Correction
Ming-Hui Liu, Harry Cheng, Xin Luo +2
To generalize deepfake detectors to future unseen forgeries, most existing methods attempt to simulate the dynamically evolving forgery types using available source domain data. Ho…
Word-Anchored Temporal Forgery Localization
Tianyi Wang, Xi Shao, Harry Cheng +2
Current temporal forgery localization (TFL) approaches typically rely on temporal boundary regression or continuous frame-level anomaly detection paradigms to derive candidate forg…
RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild
Danni Xu, Shaojing Fan, Harry Cheng +1
Multimodal misinformation increasingly leverages visual persuasion, where repurposed or manipulated images strengthen misleading text. We introduce RW-Post, a post-aligned text--im…
Suppressing Gradient Conflict for Generalizable Deepfake Detection
Ming-Hui Liu, Harry Cheng, Xin Luo +1
Robust deepfake detection models must be capable of generalizing to ever-evolving manipulation techniques beyond training data. A promising strategy is to augment the training data…
Fair Deepfake Detectors Can Generalize
Harry Cheng, Ming-Hui Liu, Yangyang Guo +3
Deepfake detection models face two critical challenges: generalization to unseen manipulations and demographic fairness among population groups. However, existing approaches often…
Learning Real Facial Concepts for Independent Deepfake Detection
Ming-Hui Liu, Harry Cheng, Tianyi Wang +2
Deepfake detection models often struggle with generalization to unseen datasets, manifesting as misclassifying real instances as fake in target domains. This is primarily due to an…
FractalForensics: Proactive Deepfake Detection and Localization via Fractal Watermarks
Tianyi Wang, Harry Cheng, Ming-Hui Liu +1
Proactive Deepfake detection via robust watermarks has seen interest ever since passive Deepfake detectors encountered challenges in identifying high-quality synthetic images. Howe…
NullSwap: Proactive Identity Cloaking Against Deepfake Face Swapping
Tianyi Wang, Harry Cheng, Xiao Zhang +1
Suffering from performance bottlenecks in passively detecting high-quality Deepfake images due to the advancement of generative models, proactive perturbations offer a promising ap…
LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks
Tianyi Wang, Mengxiao Huang, Harry Cheng +2
Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algori…
Bridging the Intent Gap: Knowledge-Enhanced Visual Generation
Yi Cheng, Ziwei Xu, Dongyun Lin +5
For visual content generation, discrepancies between user intentions and the generated content have been a longstanding problem. This discrepancy arises from two main factors. Firs…
Robust Identity Perceptual Watermark Against Deepfake Face Swapping
Tianyi Wang, Mengxiao Huang, Harry Cheng +2
Notwithstanding offering convenience and entertainment to society, Deepfake face swapping has caused critical privacy issues with the rapid development of deep generative models. D…