BZPEERPapers, people, evidence and research paths
Menu

Discover

SearchFind papers, people, topics, institutions and opportunities from one place.GraphSee how papers, authors, topics and evidence connect.

Research tools

HomeContinue from the papers, people and topics that matter now.CollectionsTurn saved papers into reading lists, evidence maps and shareable context.ResearchReconstruct the path that led you from search to paper to question.CollaborationMove from a paper, topic or expert request into a focused thread.PublishCreate full-text research articles with metadata, formulas and versions.

Network

ExpertsFind specialists whose work and reviews explain why they can help.CommunitiesJoin research groups organized around papers, topics and shared evidence.

Opportunities

ListingsBrowse roles, calls and collaborations connected to real research context.

Account

Log inReturn to your saved papers, graph views and active threads.Create accountStart saving papers, building a research trail and joining teams.
Log inCreate account

Source author record

Xin Liao

Xin Liao appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
MethodologyComputer Visioneess.IVmath.PRMultimediamath.AP

Catalog footprint

What is connected

9works
6topics
4close collaborators

Actions

Connect this record

Log in to claim
Open graphBrowse works

Research graph

See the researcher in context

Open full explorer

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

9 published item(s)

preprint2026arXiv

DDNet: A Dual-Stream Graph Learning and Disentanglement Framework for Temporal Forgery Localization

The rapid evolution of AIGC technology enables misleading viewers by tampering mere small segments within a video, rendering video-level detection inaccurate and unpersuasive. Consequently, temporal forgery localization (TFL), which aims to precisely pinpoint tampered segments, becomes critical. However, existing methods are often constrained by \emph{local view}, failing to capture global anomalies. To address this, we propose a \underline{d}ual-stream graph learning and \underline{d}isentanglement framework for temporal forgery localization (DDNet). By coordinating a \emph{Temporal Distance Stream} for local artifacts and a \emph{Semantic Content Stream} for long-range connections, DDNet prevents global cues from being drowned out by local smoothness. Furthermore, we introduce Trace Disentanglement and Adaptation (TDA) to isolate generic forgery fingerprints, alongside Cross-Level Feature Embedding (CLFE) to construct a robust feature foundation via deep fusion of hierarchical features. Experiments on ForgeryNet and TVIL benchmarks demonstrate that our method outperforms state-of-the-art approaches by approximately 9\% in AP@0.95, with significant improvements in cross-domain robustness.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Decoupling Semantics and Fingerprints: A Universal Representation for AI-Generated Image Detection

Detecting AI-generated images across unseen architectures remains challenging, as existing models often overfit to generator-specific fingerprints and semantic content rather than learning universal forgery traces. We attribute this failure to feature entanglement: detectors learn these factors as a single entangled representation, where universal forgery traces are inextricably confounded with both generator-specific fingerprints and semantic content. Crucially, our spectral analysis reveals that this entanglement is avoidable: distinct generator-specific fingerprints (e.g., GAN stripes vs. Diffusion Model spots) occupy disjoint frequency subspaces and coexist as independent superpositions. Leveraging this physical orthogonality, we propose the Orthogonal Decomposition and Purification Network (ODP-Net) to structurally disentangle these factors. Specifically, ODP-Net employs (1) Instance-aware Orthogonal Decomposition to project features into mutually exclusive subspaces: universal forgery traces, generator-specific fingerprints, and semantic content; (2) Perturbation-based Purification to enforce semantic invariance via cross-sample feature injection; and (3) Manifold Alignment to bridge domain gaps. By explicitly decoupling universal forgery traces from generator-specific fingerprints and semantic content, ODP-Net achieves state-of-the-art performance on unseen architectures (e.g., Stable Diffusion 3), validating that structural disentanglement is key to generalization.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

NiMark: A Non-intrusive Watermarking Framework against Screen-shooting Attacks

Unauthorized screen-shooting poses a critical data leakage risk. Resisting screen-shooting attacks typically requires high-strength watermark embedding, inevitably degrading the cover image. To resolve the robustness-fidelity conflict, non-intrusive watermarking has emerged as a solution by constructing logical verification keys without altering the original content. However, existing non-intrusive schemes lack the capacity to withstand screen-shooting noise. While deep learning offers a potential remedy, we observe that directly applying it leads to a previously underexplored failure mode, the Structural Shortcut: networks tend to learn trivial identity mappings and neglect the image-watermark binding. Furthermore, even when logical binding is enforced, standard training strategies cannot fully bridge the noise gap, yielding suboptimal robustness against physical distortions. In this paper, we propose NiMark, an end-to-end framework addressing these challenges. First, to eliminate the structural shortcut, we introduce the Sigmoid-Gated XOR (SG-XOR) estimator to enable gradient propagation for the logical operation, effectively enforcing rigid image-watermark binding. Second, to overcome the robustness bottleneck, we devise a two-stage training strategy integrating a restorer to bridge the domain gap caused by screen-shooting noise. Experiments demonstrate that NiMark consistently outperforms representative state-of-the-art methods against both digital attacks and screen-shooting noise, while maintaining zero visual distortion.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Asymptotics and statistical inferences on independent and non-identically distributed bivariate Gaussian triangular arrays

In this paper, we establish the first and the second-order asymptotics of distributions of normalized maxima of independent and non-identically distributed bivariate Gaussian triangular arrays, where each vector of the $n$th row follows from a bivariate Gaussian distribution with correlation coefficient being a monotone continuous function of $i/n$. Furthermore, parametric inference for this unknown function is studied. Some simulation study and real data sets analysis are also presented.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Second-order asymptotics on distributions of maxima of bivariate elliptical arrays

Let $\{ (ξ_{ni}, η_{ni}), 1\leq i \leq n, n\geq 1 \}$ be a triangular array of independent bivariate elliptical random vectors with the same distribution function as $(S_{1}, ρ_{n}S_{1}+\sqrt{1-ρ_{n}^2}S_{2})$, $ρ_{n}\in (0,1)$, where $(S_{1},S_{2})$ is a bivariate spherical random vector. For the distribution function of radius $\sqrt{S_{1}^2+S_{2}^2}$ belonging to the max-domain of attraction of the Weibull distribution, Hashorva (2006) derived the limiting distribution of maximum of this triangular array if convergence rate of $ρ_{n}$ to $1$ is given. In this paper, under the refinement of the rate of convergence of $ρ_{n}$ to $1$ and the second-order regular variation of the distributional tail of radius, precise second-order distributional expansions of the normalized maxima of bivariate elliptical triangular arrays are established.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Dynamic Bivariate Normal Copula

Normal copula with a correlation coefficient between $-1$ and $1$ is tail independent and so it severely underestimates extreme probabilities. By letting the correlation coefficient in a normal copula depend on the sample size, Hüsler and Reiss (1989) showed that the tail can become asymptotically dependent. In this paper, we extend this result by deriving the limit of the normalized maximum of $n$ independent observations, where the $i$-th observation follows from a normal copula with its correlation coefficient being either a parametric or a nonparametric function of $i/n$. Furthermore, both parametric and nonparametric inference for this unknown function are studied, which can be employed to test the condition in Hüsler and Reiss (1989). A simulation study and real data analysis are presented too.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Rates of convergence of extremes from skew normal samples

For a skew normal random sequence, convergence rates of the distribution of its partial maximum to the Gumbel extreme value distribution are derived. The asymptotic expansion of the distribution of the normalized maximum is given under an optimal choice of norming constants. We find that the optimal convergence rate of the normalized maximum to the Gumbel extreme value distribution is proportional to $1/\log n$.

0 citations0 reviewsNo code linkedOpen access