6 citations · 25 across the 35 of their papers we have counts for
20 papers · 1 filter
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…
Beyond Standard Benchmarks: A Systematic Audit of Vision-Language Model's Robustness to Natural Semantic Variation Across Diverse Tasks
Jia Chengyu, AprilPyone MaungMaung, Huy H. Nguyen +2
Recent advances in vision-language models (VLMs) trained on web-scale image-text pairs have enabled impressive zero-shot transfer across a diverse range of visual tasks. However, c…
GFT-GCN: Privacy-Preserving 3D Face Mesh Recognition with Spectral Diffusion
Hichem Felouat, Hanrui Wang, Isao Echizen
3D face recognition offers a robust biometric solution by capturing facial geometry, providing resilience to variations in illumination, pose changes, and presentation attacks. Its…
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,…
A Controllable 3D Deepfake Generation Framework with Gaussian Splatting
Wending Liu, Siyun Liang, Huy H. Nguyen +1
We propose a novel 3D deepfake generation framework based on 3D Gaussian Splatting that enables realistic, identity-preserving face swapping and reenactment in a fully controllable…
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era
Chenyang Zhu, Xing Zhang, Yuyang Sun +2
Recent advances in image generation, particularly diffusion models, have significantly lowered the barrier for creating sophisticated forgeries, making image manipulation detection…
Exploring Active Data Selection Strategies for Continuous Training in Deepfake Detection
Yoshihiko Furuhashi, Junichi Yamagishi, Xin Wang +2
In deepfake detection, it is essential to maintain high performance by adjusting the parameters of the detector as new deepfake methods emerge. In this paper, we propose a method t…
GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithm
Hanrui Wang, Ching-Chun Chang, Chun-Shien Lu +2
Deep neural networks are highly vulnerable to adversarial examples, which are inputs with small, carefully crafted perturbations that cause misclassification -- making adversarial…
Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera Systems
Muyao Niu, Zhuoxiao Li, Yifan Zhan +3
Many surveillance cameras switch between daytime and nighttime modes based on illuminance levels. During the day, the camera records ordinary RGB images through an enabled IR-cut f…
Agentic Copyright Watermarking against Adversarial Evidence Forgery with Purification-Agnostic Curriculum Proxy Learning
Erjin Bao, Ching-Chun Chang, Hanrui Wang +1
With the proliferation of AI agents in various domains, protecting the ownership of AI models has become crucial due to the significant investment in their development. Unauthorize…
LookupForensics: A Large-Scale Multi-Task Dataset for Multi-Phase Image-Based Fact Verification
Shuhan Cui, Huy H. Nguyen, Trung-Nghia Le +2
Amid the proliferation of forged images, notably the tsunami of deepfake content, extensive research has been conducted on using artificial intelligence (AI) to identify forged con…
Mitigating Backdoor Attacks using Activation-Guided Model Editing
Felix Hsieh, Huy H. Nguyen, AprilPyone MaungMaung +2
Backdoor attacks compromise the integrity and reliability of machine learning models by embedding a hidden trigger during the training process, which can later be activated to caus…
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis
Huy H. Nguyen, Junichi Yamagishi, Isao Echizen
This paper investigates the effectiveness of self-supervised pre-trained vision transformers (ViTs) compared to supervised pre-trained ViTs and conventional neural networks (ConvNe…
Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation
Fatma Shalabi, Huy H. Nguyen, Hichem Felouat +2
Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods. We have investigated a novel app…
Leveraging Chat-Based Large Vision Language Models for Multimodal Out-Of-Context Detection
Fatma Shalabi, Hichem Felouat, Huy H. Nguyen +1
Out-of-context (OOC) detection is a challenging task involving identifying images and texts that are irrelevant to the context in which they are presented. Large vision-language mo…
Surface Normal Estimation with Transformers
Barry Shichen Hu, Siyun Liang, Johannes Paetzold +3
We propose the use of a Transformer to accurately predict normals from point clouds with noise and density variations. Previous learning-based methods utilize PointNet variants to…
Generalized Deepfakes Detection with Reconstructed-Blended Images and Multi-scale Feature Reconstruction Network
Yuyang Sun, Huy H. Nguyen, Chun-Shien Lu +3
The growing diversity of digital face manipulation techniques has led to an urgent need for a universal and robust detection technology to mitigate the risks posed by malicious for…
How Close are Other Computer Vision Tasks to Deepfake Detection?
Huy H. Nguyen, Junichi Yamagishi, Isao Echizen
In this paper, we challenge the conventional belief that supervised ImageNet-trained models have strong generalizability and are suitable for use as feature extractors in deepfake…
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…