5 citations · 8 across the 14 of their papers we have counts for
15 papers
Does Adversarial Training Improve Generalization in Multi-View VLAs? Revealing and Mitigating View Collapse
Futa Waseda, Shuhei Kurita, Isao Echizen
Vision-language-action (VLA) models adapt pretrained vision-language models (VLMs) for closed-loop robot control, transferring their perceptual and semantic capabilities to action…
AngelFingerprint: A Traceable, Explainable, and White-Box Stealthy Watermark for Text-Guided Image Editing
Bo-Han Kung, Futa Waseda, Ching-Chun Chang +2
Text-guided diffusion editing raises disinformation concerns, making reliable image provenance essential. While watermarks are commonly used for this purpose, most methods carry a…
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…
Read or Ignore? A Unified Benchmark for Typographic-Attack Robustness and Text Recognition in Vision-Language Models
Futa Waseda, Shojiro Yamabe, Daiki Shiono +2
Large vision-language models (LVLMs) are vulnerable to typographic attacks, where misleading text inserted into an image can override visual understanding. However, existing evalua…
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…
Quality Text, Robust Vision: The Role of Language in Enhancing Visual Robustness of Vision-Language Models
Futa Waseda, Saku Sugawara, Isao Echizen
Defending pre-trained vision-language models (VLMs), such as CLIP, against adversarial attacks is crucial, as these models are widely used in diverse zero-shot tasks, including ima…
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…
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships
Futa Waseda, Antonio Tejero-de-Pablos, Isao Echizen
Pre-trained vision-language (VL) models are highly vulnerable to adversarial attacks. However, existing defense methods primarily focus on image classification, overlooking two key…
Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off
Futa Waseda, Ching-Chun Chang, Isao Echizen
Adversarial training often suffers from a robustness-accuracy trade-off, where achieving high robustness comes at the cost of accuracy. One approach to mitigate this trade-off is l…
Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection
Lukas Strack, Futa Waseda, Huy H. Nguyen +2
Infrared detection is an emerging technique for safety-critical tasks owing to its remarkable anti-interference capability. However, recent studies have revealed that it is vulnera…
Beyond In-Domain Scenarios: Robust Density-Aware Calibration
Christian Tomani, Futa Waseda, Yuesong Shen +1
Calibrating deep learning models to yield uncertainty-aware predictions is crucial as deep neural networks get increasingly deployed in safety-critical applications. While existing…
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently
Futa Waseda, Sosuke Nishikawa, Trung-Nghia Le +2
Deep neural networks are vulnerable to adversarial examples (AEs), which have adversarial transferability: AEs generated for the source model can mislead another (target) model's p…