6 citations · 16 across the 22 of their papers we have counts for
22 papers
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…
Retrosynthesis of Synthetic Media for Explainable AI Provenance Forensics
Yijie Lin, Ching-Chun Chang, Isao Echizen +2
With the rapid proliferation of generative models on Machine Learning as a Service (MLaaS) platforms, reliably tracing the provenance of synthetic media without modifying generator…
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…
On the Origin of Synthetic Information by Means of Steganographic Inheritance
Ching-Chun Chang, Isao Echizen
The origin of species has been the mystery of mysteries in natural science. By analogy, the origin of synthetic information, we suggest, is the mystery of mysteries in information…
Role-Aware Artificial Intelligence Across Augmentation and Automation in Human-Machine Symbiosis
Ching-Chun Chang, Yuchen Guo, Hanrui Wang +2
The evolution of artificial intelligence (AI) has rendered the boundary between humanity and computational machinery increasingly ambiguous. In the presence of more interwoven rela…
EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection
Chenyang Zhu, Maorong Wang, Jun Liu +2
The rapid proliferation of AI-Generated Images (AIGIs) poses severe misinformation risks, making AIGI detection critical yet challenging. Traditional detection paradigms mainly rel…
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,…
Tell-Tale Watermarks for Explanatory Reasoning in Synthetic Media Forensics
Ching-Chun Chang, Isao Echizen
The rise of synthetic media has blurred the boundary between reality and fabrication under the evolving power of artificial intelligence, fueling an infodemic that erodes public tr…
Measuring Human Involvement in AI-Generated Text: A Case Study on Academic Writing
Yuchen Guo, Zhicheng Dou, Huy H. Nguyen +3
Content creation has dramatically progressed with the rapid advancement of large language models like ChatGPT and Claude. While this progress has greatly enhanced various aspects o…
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…
Chronology of Multi-Agent Interactions for Provenance of Evolving Information
Ching-Chun Chang, Isao Echizen
Provenance is the chronological history of things, resonating with the fundamental pursuit to uncover origins, trace connections, and situate entities within the flow of space and…
Steganography Beyond Space-Time with Chain of Multimodal AI
Ching-Chun Chang, Isao Echizen
Steganography is the art and science of covert writing, with a broad range of applications interwoven within the realm of cybersecurity. As artificial intelligence continues to evo…
Cyber-Physical Steganography in Robotic Motion Control
Ching-Chun Chang, Yijie Lin, Isao Echizen
Steganography, the art of information hiding, has continually evolved across visual, auditory and linguistic domains, adapting to the ceaseless interplay between steganographic con…
Imitation Game for Adversarial Disillusion with Chain-of-Thought Reasoning in Generative AI
Ching-Chun Chang, Fan-Yun Chen, Shih-Hong Gu +3
As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial attacks manifest in two primary f…
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…
Steganography in Game Actions
Ching-Chun Chang, Isao Echizen
The exchange of messages has always carried with it the timeless challenge of secrecy. From whispers in shadows to the enigmatic notes written in the margins of history, humanity h…
Hypnopaedia-Aware Machine Unlearning via Psychometrics of Artificial Mental Imagery
Ching-Chun Chang, Kai Gao, Shuying Xu +3
Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of arti…
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…
Minimal Cascade Gradient Smoothing for Fast Transferable Preemptive Adversarial Defense
Hanrui Wang, Ching-Chun Chang, Chun-Shien Lu +3
Adversarial attacks persist as a major challenge in deep learning. While training- and test-time defenses are well-studied, they often reduce clean accuracy, incur high cost, or fa…
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…
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…
Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis
Zhicheng Dou, Yuchen Guo, Ching-Chun Chang +2
The emergence of large language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4) used by ChatGPT, has profoundly impacted the academic and broader community. Whi…