6 papers
Is Our Benchmark Enough? An Analysis of Continual Learning for MLLMs
Van-Tuan Tran, Shruthi Gowda, Merim Dzaferagic +1
Continual adaptation is essential for multimodal large language models (MLLMs) deployed across evolving domains, but the state-of-the-art MR-LoRA method highly relies on the assump…
Transferable Multi-Bit Watermarking Across Frozen Diffusion Models via Latent Consistency Bridges
Hong-Hanh Nguyen-Le, Van-Tuan Tran, Thuc D. Nguyen +1
As generative AI advances, global governance frameworks increasingly mandate verifiable content provenance. However, existing watermarking techniques face a critical policy-to-tech…
Beyond Binary Classification: A Semi-supervised Approach to Generalized AI-generated Image Detection
Hong-Hanh Nguyen-Le, Van-Tuan Tran, Dinh-Thuc Nguyen +1
The rapid advancement of generators (e.g., StyleGAN, Midjourney, DALL-E) has produced highly realistic synthetic images, posing significant challenges to digital media authenticity…
ToFU: Transforming How Federated Learning Systems Forget User Data
Van-Tuan Tran, Hong-Hanh Nguyen-Le, Quoc-Viet Pham
Neural networks unintentionally memorize training data, creating privacy risks in federated learning (FL) systems, such as inference and reconstruction attacks on sensitive data. T…
Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation
Hong-Hanh Nguyen-Le, Van-Tuan Tran, Dinh-Thuc Nguyen +1
Deepfake (DF) detectors face significant challenges when deployed in real-world environments, particularly when encountering test samples deviated from training data through either…
Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey
Hong-Hanh Nguyen-Le, Van-Tuan Tran, Dinh-Thuc Nguyen +1
In recent years, deepfakes (DFs) have been utilized for malicious purposes, such as individual impersonation, misinformation spreading, and artists style imitation, raising questio…