arXiv subjects

Lei Shi

Publications and source records attributed to Lei Shi.

3 recordsLinked to original sources

arXiv2026

Fine-Grained Anomaly Perception in Wild UGC-Enhanced Images: A Comprehensive Dataset and Difference-Fusion Framework

Image enhancement and restoration have become standard back-end operations on short-video and social media platforms to boost UGC visual experience. Yet these processes inevitably introduce visual anomalies--especially in faces, texts, and textures--that directly undermine perceptual fidelity and viewer trust. While existing IQA methods perform well on classic distortions, they target holistic quality assessment and fail to capture the specific, localized anomalies caused by enhancement algorithms in real-world UGC. To bridge this gap, we formally define a new task-quality Anomaly Perception for UGC image Enhancement (UEAP), and contribute the first UEAP benchmark dataset, named UEAP-4k, curated from the real business scenarios. It provides fine-grained annotations for anomaly categories, localization and severity levels. Furthermore, we propose a Difference-Fusion Anomaly Perception Method (DFAP-UGC) for wild UGC-enhanced images, which leverages explicit problem-reference difference fusion with dense spatial querying, regional verification, and quality-aware ranking, enabling robust anomaly identification in challenging scenarios. To handle the inherent coupling of subtasks in this new task, we propose a Locality-Aware Dynamic Task Prioritization (LADTP) training strategy that enables effective end-to-end learning and eliminates multi-stage overhead. Extensive experiments show that our method outperforms baselines adapted from classical approaches for this task, validating the value of this dataset and the superior of DFAP-UGC for robust UGC-enhanced image anomaly perception. Code and data will be public.

cs.CV↗

arXiv2026

Variation Spaces for Encoder--Decoder Neural Operators: Approximation and Generalization

Inspired by the function-space theory of neural networks, we formulate and analyze a variation space for nonlinear operators between Hilbert spaces, defined through vector-valued Borel measures of bounded variation. We characterize its unit ball as the closed convex hull of a vector-valued single-neuron dictionary in Bochner spaces. For the ReLU activation, the bounded linear operators in this space are precisely the Schatten-$1$ operators, with equivalent norms. For operators in this space, we establish encoder--decoder approximation bounds in the Bochner $L^q$-norm, where the error decomposes into input and output encoding errors and a finite-width term of order $N^{-1/2}$. Under sub-Gaussian assumptions on the input and noise, we further derive high-probability generalization bounds for empirical least squares over path-norm-constrained encoder--decoder networks; the finite-sample contribution to the squared prediction error is of order $K^{-1/2}$ up to logarithmic factors. The finite-width and finite-sample constants are independent of the encoding dimensions and bases, with the latter also independent of the network width. When the encoding errors decay algebraically, these bounds yield algebraic approximation and learning rates, in contrast to the complexity barriers for Lipschitz and Fréchet differentiable operator classes.

stat.ML↗

arXiv2026

Online Safety Regulation Increases Attention to VPNs: Privacy Implications of the UK Online Safety Act

Governments worldwide are increasingly regulating digital platforms to reduce online harms, but access restrictions can alter user behaviour and create new privacy risks. The UK Online Safety Act, passed in 2023, rolled out in phases - illegal-content enforcement in March 2025 and mandatory age verification in July 2025. We analyse Reddit discourse across VPN and UK Politics communities and conduct a privacy-policy risk analysis of 69 VPN services. We find that the behavioural response is concentrated at the July 2025 deadline, when platforms hosting pornographic content were required to deploy age checks. UK VPN search interest on Google increased by 147% at this deadline. UK-resident users' VPN-subreddit activity increased by 145%. Their regulatory- or privacy-related VPN posts and comments rose by 1,265% at this deadline. UK Politics communities show the same concentration at a larger magnitude, with OSA-related political discourse rising by 1481%. These effects were far smaller or statistically indistinguishable from pre-existing trends at Royal Assent and the illegal-harms enforcement deadline, indicating that the deployed age checks drove the response. Users primarily frame this response around privacy, surveillance, and distrust of age-verification intermediaries rather than access-seeking, with near-zero genuine pro-OSA sentiment across two independent classifiers. Several users noted that those least able to pay for reputable VPNs are most likely to turn to free services that monetise their data. Search attention increases across all disclosed privacy-risk categories, with no evidence of a shift toward higher-risk VPN providers. Crucially, after a full year, this attention is still elevated, arguing against a temporary news-cycle reaction. Thus, online safety regulation may create secondary privacy costs without disproportionately directing attention toward higher-risk VPNs.

cs.CY↗