6 papers
Semantic Color Naturalness Breaker: Preventing Illegitimate Colorization via Content-Aware Color Priors
Yuki Nii, Futa Waseda, Ching-Chun Chang +1
Automatic image colorization enables large-scale and low-cost reuse of grayscale media (e.g., manga panels and archival photographs), facilitating unauthorized reuse and redistribu…
Rethinking Brain Decoding with CLIP: The Role of Adversarial Robustness
Byeongseo Bok, Futa Waseda, Jun Liu +1
Brain decoding aims to uncover neural mechanisms by inferring stimulus-related representations from brain signals. In fMRI studies, this is typically achieved by mapping fMRI respo…
Text-Printed Image: Bridging the Image-Text Modality Gap for Text-centric Training of Large Vision-Language Models
Shojiro Yamabe, Futa Waseda, Daiki Shiono +1
Recent large vision-language models (LVLMs) have been applied to diverse VQA tasks. However, achieving practical performance typically requires task-specific fine-tuning with large…
Uncolorable Examples: Preventing Unauthorized AI Colorization via Perception-Aware Chroma-Restrictive Perturbation
Yuki Nii, Futa Waseda, Ching-Chun Chang +1
AI-based colorization has shown remarkable capability in generating realistic color images from grayscale inputs. However, it poses risks of copyright infringement -- for example,…
Understanding Sensitivity of Differential Attention through the Lens of Adversarial Robustness
Tsubasa Takahashi, Shojiro Yamabe, Futa Waseda +1
Differential Attention (DA) has been proposed as a refinement to standard attention, suppressing redundant or noisy context through a subtractive structure and thereby reducing con…
MergePrint: Merge-Resistant Fingerprints for Robust Black-box Ownership Verification of Large Language Models
Shojiro Yamabe, Futa Waseda, Tsubasa Takahashi +1
Protecting the intellectual property of Large Language Models (LLMs) has become increasingly critical due to the high cost of training. Model merging, which integrates multiple exp…