Source author record

Jiaqi Wu

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

ResearcherUnclaimed source record

Artificial Intelligence Computer Vision Cryptography and Security physics.optics

Catalog footprint

What is connected

3works

4topics

4close collaborators

Actions

Connect this record

Open graph Browse works

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.

preprint2026arXiv

GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment

The release of GPT-image-2 by OpenAI marks a watershed moment in AI-generated imagery: the boundary between photographic reality and synthetic content has never been more difficult to discern. We introduce the GPT-Image-2 Twitter Dataset, the first published dataset of GPT-image-2 generated images, sourced from publicly available Twitter/X posts in the immediate aftermath of the model's April 21, 2026 release. Leveraging the Twitter API v2 and a multi-stage curation pipeline spanning multilingual text heuristics (English, Japanese, and Chinese), browser-automated Twitter "Made with AI" badge verification, and model name variant matching, we curate 10,217 confirmed GPT-image-2 images from 27,662 collected records over a six-day window. We characterize the dataset across four analyses: CLIP-based zero-shot subject taxonomy, OCR text legibility (82.0% of images contain detectable text), face detection (59.2% of images, 22,583 total faces), and semantic clustering (137 CLIP ViT-L/14 clusters). A key negative result is that C2PA content credentials are systematically stripped by Twitter's CDN on upload, rendering cryptographic provenance verification infeasible for social-media-sourced AI images. The dataset and all curation code are released publicly.

preprint2022arXiv

High-order harmonic generation in X-ray range from laser induced noble gas multivalent ions

Sub-femtosecond x-ray burst is powerful tool for probing and imaging electronic and concomitant atomic motion in attosecond physics. For years, x-ray source (above 2 keV) had mainly been obtained from X-ray free electron laser (XFEL) or synchrotron radiation, which are high energy consumption, high cost and huge volume. Here we propose a low-cost and small-size method to generate X-ray source. We experimentally obtained high photon energy spectrum (~ 5.2 keV) through both atom and multiple valence state ions using a near-infrared 1.45 μm driving laser interacting with krypton gas, according to our knowledge, which is the highest photon energy generated through high-order harmonic generation up to now. In our scheme, multi-keV photon energy can be achieved with a relaxed requirement on experimental conditions, and make time-resolved studies more accessible to many laboratories that are capable of producing high energy photon extending to hard x-ray region. Furthermore, our scheme minimizes the influence of X-ray fluorescence process on detection, and can also be utilized to study the quantum-optical nature of high-order harmonic generation.

preprint2021arXiv

Privacy Information Classification: A Hybrid Approach

A large amount of information has been published to online social networks every day. Individual privacy-related information is also possibly disclosed unconsciously by the end-users. Identifying privacy-related data and protecting the online social network users from privacy leakage turn out to be significant. Under such a motivation, this study aims to propose and develop a hybrid privacy classification approach to detect and classify privacy information from OSNs. The proposed hybrid approach employs both deep learning models and ontology-based models for privacy-related information extraction. Extensive experiments are conducted to validate the proposed hybrid approach, and the empirical results demonstrate its superiority in assisting online social network users against privacy leakage.