arXiv subjects

Boquan Li

Publications and source records attributed to Boquan Li.

5 recordsLinked to original sources

arXiv2026

SVOM Ground Support System

The Ground Support System (GSS) is a critical component of the Space-based multi-band astronomical Variable Objects Monitor (SVOM) mission's ground segment. Its main tasks include organizing and implementing the operations and management of the SVOM payloads, receiving scientific data, as well as preprocessing and managing the scientific data. To address the specific requirements of the SVOM mission, including specialized mission planning, data reception, status monitoring, and product processing, a dedicated SVOM Ground Support System has been designed based on the integrated architecture of the Chinese Space Science Satellite Project Ground Support System. This system has successfully supported SVOM's launch and on-orbit operations.

astro-ph.IM↗

arXiv2026

SVOM Real-time Response and Collaboration System

The SVOM mission (Space-based multi-band astronomical Variable Objects Monitor) is a Franco-Chinese mission dedicated to the study of the most distant explosions of stars, the gamma-ray bursts. Here, we introduce the real-time response and collaboration system of SVOM, with the adoption of the BeiDou-3 short message communication service. We present the SVOM on-board and on-ground system designs and data flow, together with the collaboration mechanism with other missions. In the first year of the in-flight operation, SVOM has detected 172 gamma-ray bursts, including 147 by the GRM instrument and 62 by the ECLAIRs instrument. At the same time, SVOM has performed 1040 observations, including 122 ToO-EX(Target of Opportunity-Exceptional) observations, 48 ToO-MM(Target of Opportunity-Multi-messenger) observations and 870 ToO-NOM(Target of Opportunity-Nominal) observations. All these have increased the scientific output of the mission.

astro-ph.IM↗

arXiv2025

SpeechForensics: Audio-Visual Speech Representation Learning for Face Forgery Detection

Detection of face forgery videos remains a formidable challenge in the field of digital forensics, especially the generalization to unseen datasets and common perturbations. In this paper, we tackle this issue by leveraging the synergy between audio and visual speech elements, embarking on a novel approach through audio-visual speech representation learning. Our work is motivated by the finding that audio signals, enriched with speech content, can provide precise information effectively reflecting facial movements. To this end, we first learn precise audio-visual speech representations on real videos via a self-supervised masked prediction task, which encodes both local and global semantic information simultaneously. Then, the derived model is directly transferred to the forgery detection task. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in terms of cross-dataset generalization and robustness, without the participation of any fake video in model training. Code is available at https://github.com/Eleven4AI/SpeechForensics.

cs.CV↗

arXiv2024

Efficient and Universal Watermarking for LLM-Generated Code Detection

Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty and the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plug-and-play watermarking approach for AI-generated code detection, named ACW (AI Code Watermarking). ACW is training-free and works by selectively applying a set of carefully-designed, semantic-preserving and idempotent code transformations to LLM code outputs. The presence or absence of the transformations serves as implicit watermarks, enabling the detection of AI-generated code. Our experimental results show that ACW effectively and efficiently detects AI-generated code, preserves code utility, and is resilient against potential code disruptions. Especially, ACW is universal across different LLMs, addressing the limitations of existing approaches.

cs.CR↗

arXiv2023

How Generalizable are Deepfake Image Detectors? An Empirical Study

Deepfakes are becoming increasingly credible, posing a significant threat given their potential to facilitate fraud or bypass access control systems. This has motivated the development of deepfake detection methods, in which deep learning models are trained to distinguish between real and synthesized footage. Unfortunately, existing detectors struggle to generalize to deepfakes from datasets they were not trained on, but little work has been done to examine why or how this limitation can be addressed. Especially, those single-modality deepfake images reveal little available forgery evidence, posing greater challenges than detecting deepfake videos. In this work, we present the first empirical study on the generalizability of deepfake detectors, an essential goal for detectors to stay one step ahead of attackers. Our study utilizes six deepfake datasets, five deepfake image detection methods, and two model augmentation approaches, confirming that detectors do not generalize in zero-shot settings. Additionally, we find that detectors are learning unwanted properties specific to synthesis methods and struggling to extract discriminative features, limiting their ability to generalize. Finally, we find that there are neurons universally contributing to detection across seen and unseen datasets, suggesting a possible path towards zero-shot generalizability.

cs.CV↗