1 citations · 1 across the 2 of their papers we have counts for
6 papers
MaXsive: High-Capacity and Robust Training-Free Generative Image Watermarking in Diffusion Models
Po-Yuan Mao, Cheng-Chang Tsai, Chun-Shien Lu
The great success of the diffusion model in image synthesis led to the release of gigantic commercial models, raising the issue of copyright protection and inappropriate content ge…
VSC: Visual Search Compositional Text-to-Image Diffusion Model
Do Huu Dat, Nam Hyeonu, Po-Yuan Mao +1
Text-to-image diffusion models have shown impressive capabilities in generating realistic visuals from natural-language prompts, yet they often struggle with accurately binding att…
Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!
Shashank Kotyan, Po-Yuan Mao, Pin-Yu Chen +1
Deep neural networks can be exploited using natural adversarial samples, which do not impact human perception. Current approaches often rely on deep neural networks' white-box natu…
Synthetic Shifts to Initial Seed Vector Exposes the Brittle Nature of Latent-Based Diffusion Models
Mao Po-Yuan, Shashank Kotyan, Tham Yik Foong +1
Recent advances in Conditional Diffusion Models have led to substantial capabilities in various domains. However, understanding the impact of variations in the initial seed vector…
The Challenges of Image Generation Models in Generating Multi-Component Images
Tham Yik Foong, Shashank Kotyan, Po Yuan Mao +1
Recent advances in text-to-image generators have led to substantial capabilities in image generation. However, the complexity of prompts acts as a bottleneck in the quality of imag…
HOPE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts
Do Huu Dat, Po Yuan Mao, Tien Hoang Nguyen +2
Compositional Zero-Shot Learning (CZSL) has emerged as an essential paradigm in machine learning, aiming to overcome the constraints of traditional zero-shot learning by incorporat…