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Taihui Li

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3 papers

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TMLR Journal 2025 Journal Article

A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior

Hengyue Liang
Taihui Li
Ju Sun

Image watermarks have been considered a promising technique to help detect AI-generated content, which can be used to protect copyright or prevent fake image abuse. In this work, we present a black-box method for removing invisible image watermarks, without the need of any dataset of watermarked images or any knowledge about the watermark system. Our approach is simple to implement: given a single watermarked image, we regress it by deep image prior (DIP). We show that from the intermediate steps of DIP one can reliably find an evasion image that can remove invisible watermarks while preserving high image quality. Due to its unique working mechanism and practical effectiveness, we advocate including DIP as a baseline invasion method for benchmarking the robustness of watermarking systems. Finally, by showing the limited ability of DIP and other existing black-box methods in evading training-based visible watermarks, we discuss the positive implications on the practical use of training-based visible watermarks to prevent misinformation abuse.

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NeurIPS Conference 2024 Conference Paper

DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models

Hengkang Wang
Xu Zhang
Taihui Li
Yuxiang Wan
Tiancong Chen
Ju Sun

Pretrained diffusion models (DMs) have recently been popularly used in solving inverse problems (IPs). The existing methods mostly interleave iterative steps in the reverse diffusion process and iterative steps to bring the iterates closer to satisfying the measurement constraint. However, such interleaving methods struggle to produce final results that look like natural objects of interest (i. e. , manifold feasibility) and fit the measurement (i. e. , measurement feasibility), especially for nonlinear IPs. Moreover, their capabilities to deal with noisy IPs with unknown types and levels of measurement noise are unknown. In this paper, we advocate viewing the reverse process in DMs as a function and propose a novel plug-in method for solving IPs using pretrained DMs, dubbed DMPlug. DMPlug addresses the issues of manifold feasibility and measurement feasibility in a principled manner, and also shows great potential for being robust to unknown types and levels of noise. Through extensive experiments across various IP tasks, including two linear and three nonlinear IPs, we demonstrate that DMPlug consistently outperforms state-of-the-art methods, often by large margins especially for nonlinear IPs.

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TMLR Journal 2023 Journal Article

Early Stopping for Deep Image Prior

Hengkang Wang
Taihui Li
Zhong Zhuang
Tiancong Chen
Hengyue Liang
Ju Sun

Deep image prior (DIP) and its variants have shown remarkable potential to solve inverse problems in computational imaging (CI), needing no separate training data. Practical DIP models are often substantially overparameterized. During the learning process, these models first learn the desired visual content and then pick up potential modeling and observational noise, i.e., performing early learning then overfitting. Thus, the practicality of DIP hinges on early stopping (ES) that can capture the transition period. In this regard, most previous DIP works for CI tasks only demonstrate the potential of the models, reporting the peak performance against the ground truth but providing no clue about how to operationally obtain near-peak performance without access to the ground truth. In this paper, we set to break this practicality barrier of DIP, and propose an effective ES strategy that consistently detects near-peak performance across several CI tasks and DIP variants. Simply based on the running variance of DIP intermediate reconstructions, our ES method not only outpaces the existing ones---which only work in very narrow regimes, but also remains effective when combined with methods that try to mitigate overfitting.

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