arXiv subjects

Qiu Wang

Publications and source records attributed to Qiu Wang.

7 recordsLinked to original sources

arXiv2026

LoRA-Key: User-Centric LoRA Watermarking for Text-to-Image Diffusion Models

Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing text-to-image diffusion models, enabling lightweight modules that are shared, reused, and commercialized as independent assets. This LoRA-centric ecosystem shifts copyright protection from foundation models to distributed LoRA modules, which are easy to copy, redistribute, or reuse without authorization. Existing watermarking methods either protect the base diffusion model or require watermark-aware retraining for each target LoRA, limiting their practicality in open community settings. To address this limitation, we propose LoRA-Key, a user-centric LoRA watermarking framework that treats copyright protection as a reusable ownership key. LoRA-Key encapsulates a recoverable secret message into a standalone user-specific Watermark LoRA, which can be attached to different target LoRAs through training-free linear superposition without per-LoRA retraining or structural modification. To train such a reusable key, we first establish a latent watermark prior in the frozen VAE latent space for robust message embedding and recovery, and then optimize the Watermark LoRA with message-conditioned watermark supervision and semantic consistency constraints. We further introduce Gradient Orthogonal Projection (GOP) to suppress watermark updates that conflict with semantic-preserving directions, reducing interference with generation fidelity and downstream style adaptation. Extensive experiments show that LoRA-Key provides lightweight plug-and-play copyright protection while preserving generation quality and style fidelity, and maintains robust ownership verification under image-level distortions, downstream fine-tuning, and multi-LoRA composition.

cs.CR↗

arXiv2026

R$^2$BD: A Reconstruction-Based Method for Generalizable and Efficient Detection of Fake Images

Recently, reconstruction-based methods have gained attention for AIGC image detection. These methods leverage pre-trained diffusion models to reconstruct inputs and measure residuals for distinguishing real from fake images. Their key advantage lies in reducing reliance on dataset-specific artifacts and improving generalization under distribution shifts. However, they are limited by significant inefficiency due to multi-step inversion and reconstruction, and their reliance on diffusion backbones further limits generalization to other generative paradigms such as GANs. In this paper, we propose a novel fake image detection framework, called R$^2$BD, built upon two key designs: (1) G-LDM, a unified reconstruction model that simulates the generation behaviors of VAEs, GANs, and diffusion models, thereby broadening the detection scope beyond prior diffusion-only approaches; and (2) a residual bias calculation module that distinguishes real and fake images in a single inference step, which is a significant efficiency improvement over existing methods that typically require 20$+$ steps. Extensive experiments on the benchmark from 10 public datasets demonstrate that R$^2$BD is over 22$\times$ faster than existing reconstruction-based methods while achieving superior detection accuracy. In cross-dataset evaluations, it outperforms state-of-the-art methods by an average of 13.87\%, showing strong efficiency and generalization across diverse generative methods. The code and dataset used for evaluation are available at https://github.com/QingyuLiu/RRBD.

cs.CV↗

arXiv2025

PT-Mark: Invisible Watermarking for Text-to-image Diffusion Models via Semantic-aware Pivotal Tuning

Watermarking for diffusion images has drawn considerable attention due to the widespread use of text-to-image diffusion models and the increasing need for their copyright protection. Recently, advanced watermarking techniques, such as Tree Ring, integrate watermarks by embedding traceable patterns (e.g., Rings) into the latent distribution during the diffusion process. Such methods disrupt the original semantics of the generated images due to the inevitable distribution shift caused by the watermarks, thereby limiting their practicality, particularly in digital art creation. In this work, we present Semantic-aware Pivotal Tuning Watermarks (PT-Mark), a novel invisible watermarking method that preserves both the semantics of diffusion images and the traceability of the watermark. PT-Mark preserves the original semantics of the watermarked image by gradually aligning the generation trajectory with the original (pivotal) trajectory while maintaining the traceable watermarks during whole diffusion denoising process. To achieve this, we first compute the salient regions of the watermark at each diffusion denoising step as a spatial prior to identify areas that can be aligned without disrupting the watermark pattern. Guided by the region, we then introduce an additional pivotal tuning branch that optimizes the text embedding to align the semantics while preserving the watermarks. Extensive evaluations demonstrate that PT-Mark can preserve the original semantics of the diffusion images while integrating robust watermarks. It achieves a 10% improvement in the performance of semantic preservation (i.e., SSIM, PSNR, and LPIPS) compared to state-of-the-art watermarking methods, while also showing comparable robustness against real-world perturbations and four times greater efficiency.

cs.CR↗

arXiv2024

Uncovering the Maximum Chirality in Dielectric Nanostructures

Maximum structural chirality refers to the highest selectivity for circularly polarized light (CPL) in nanostructures, often manifested as maximum circular dichroism (CD), optical rotation (OR), and spin-orbit coupling (SOC). However, the underlying physical mechanisms that lead to maximum chirality remain unclear. In this work, we demonstrate that maximum chirality in dielectric nanostructures arises from the constructive and destructive interference of multipole moments with different CPL. By employing generalized multipole decomposition, we introduce a generalized chiral multipole mechanism that allows for direct numerical calculation of CD and establishes the conditions required to achieve maximum chirality. This approach provides a comprehensive framework for analyzing chirality and serves as a foundation for future investigations of chiral nanostructures.

physics.optics↗

arXiv2024

An Experimental Configuration to Study High-Enthalpy Radiating Flows Under Nonequilibrium De-excitation

This paper introduces an experimental configuration, called the Prandtl-Meyer plus duct arrangement (PMD), designed to study high-enthalpy radiating flows undergoing nonequilibrium de-excitation. The original design proposed by Wilson, developed without the benefit of modern computational fluid dynamics (CFD), was inadequate for generating a sufficiently large undisturbed zone or achieving a uniform flow along the centerline, necessitating further refinement. Consequently, significant modifications were implemented to enhance PMD's performance, resulting in an expanded undisturbed zone and a uniform centerline flow that facilitate the measurements of nonequilibrium de-excitation.} A general design method is introduced, combining theoretical analysis and numerical simulations to tailor the flow conditions for various research objectives. The implementation involves considerations of the shock tube conditions, PMD configuration, and the effective measurement zone. The interplay between shock tube conditions and airfoil geometry generates diverse de-excitation patterns. The shock tube test time, transition onset location, and radiance intensity determine the effective measurement zone. An example utilizing N2 as the test gas demonstrates the method, achieving one-dimensional flow with thermal nonequilibrium and chemical freezing along the centerline, validating the method's effectiveness. An effective measurement zone of 200 mm is obtained under this condition, and the primary constraint under high-enthalpy conditions is the limited shock tube test time due to the high shock velocity and low fill pressure.

physics.flu-dyn↗

arXiv2021

Completion Time Minimization in Wireless-Powered UAV-Assisted Data Collection System (Full Version)

In unmanned aerial vehicle (UAV)-assisted data collection system, UAVs can be deployed to charge ground terminals (GTs) via wireless power transfer (WPT) and collect data from them via wireless information transmission (WIT). In this paper, we aim to minimize the time required by a UAV via jointly optimizing the trajectory of the UAV and the transmission scheduling for all the GTs. This problem is formulated as a mixed integer nonlinear programming (MINLP) which are difficult to address in general. To this end, we develop an iterative algorithm based on binary search and successive convex optimization (SCO) to solve it. The simulation shows that our proposed solution outperforms the benchmark algorithms.

eess.SY↗

arXiv2018

Connectivity of Underlay Cognitive Radio Networks with Directional Antennas

In cognitive radio networks (CRNs), the connectivity of secondary users (SUs) is difficult to be guaranteed due to the existence of primary users (PUs). Most prior studies only consider cognitive radio networks equipped with omni-directional antennas causing high interference at SUs. We name such CRNs with omni-directional antennas as Omn-CRNs. Compared with an omni-directional antenna, a directional antenna can concentrate the transmitting/receiving capability at a certain direction, consequently resulting in less interference. In this paper, we investigate the connectivity of SUs in CRNs with directional antennas (named as Dir-CRNs). In particular, we derive closed-form expressions of the connectivity of SUs of both Dir-CRNs and Omn-CRNs, thus enabling tractability. We show that the connectivity of SUs is mainly affected by two constraints: the spectrum availability of SUs and the topological connectivity of SUs. Extensive simulations validate the accuracy of our proposed models. Meanwhile, we also show that Dir-CRNs can have higher connectivity than Omn-CRNs mainly due to the lower interference, the higher spectrum availability and the higher topological connectivity brought by directional antennas.

cs.NI↗