Celong Liu

22 papers A* 3Journal 15Unranked 4
YearRankTypeTitle / Venue / Authors
2026 J jnl
CoRR
FSVideo Team, Qingyu Chen, Zhiyuan Fang, Haibin Huang, Xinwei Huang, Tong Jin, Minxuan Lin, Bo Liu, Celong Liu, Chongyang Ma, Xing Mei, Xiaohui Shen, Yaojie Shen, Fuwen Tan, Angtian Wang, Xiao Yang, Yiding Yang, Jiamin Yuan, Lingxi Zhang, Yuxin Zhang
2025 J jnl
CoRR
Vidi Team, Celong Liu, Chia-Wen Kuo, Chuang Huang, Dawei Du, Fan Chen, Guang Chen, Haoji Zhang, Haojun Zhao, Lingxi Zhang, Lu Guo, Lusha Li, Longyin Wen, Qihang Fan, Qingyu Chen, Rachel Deng, Sijie Zhu, Stuart Siew, Tong Jin, Weiyan Tao, Wen Zhong, Xiaohui Shen, Xin Gu, Zhenfang Chen, Zuhua Lin
2024 J jnl
CoRR
Chenglin Yang, Celong Liu, Xueqing Deng, Dongwon Kim, Xing Mei, Xiaohui Shen, Liang-Chieh Chen
2024 A* conf
SIGGRAPH Asia
Luchuan Song, Lele Chen, Celong Liu, Pinxin Liu, Chenliang Xu
2024 J jnl
CoRR
Luchuan Song, Lele Chen, Celong Liu, Pinxin Liu, Chenliang Xu
2024 J jnl
CoRR
Luchuan Song, Pinxin Liu, Lele Chen, Celong Liu, Chenliang Xu
2023 A* conf
CVPR
Han Yan, Celong Liu, Chao Ma, Xing Mei
2023 J jnl
IEEE Trans. Vis. Comput. Graph.
Celong Liu, Lingyu Wang, Zhong Li, Shuxue Quan, Yi Xu
2022 conf
EGSR (ST)
Zhong Li, Liangchen Song, Celong Liu, Junsong Yuan, Yi Xu
2021 J jnl
Comput. Graph.
Zhong Li, Lele Chen, Celong Liu, Fuyao Zhang, Zekun Li, Yu Gao, Yuanzhou Ha, Chenliang Xu, Shuxue Quan, Yi Xu
2021 A* conf
ACM Multimedia
Liangchen Song, Sheng Liu, Celong Liu, Zhong Li, Yuqi Ding, Yi Xu, Junsong Yuan
2021 J jnl
CoRR
Celong Liu, Zhong Li, Junsong Yuan, Yi Xu
2020 conf
VR Workshops
Celong Liu, Zhong Li, Shuxue Quan, Yi Xu
2020 conf
ECCV (9)
Lele Chen, Guofeng Cui, Celong Liu, Zhong Li, Ziyi Kou, Yi Xu, Chenliang Xu
2020 J jnl
CoRR
Lele Chen, Guofeng Cui, Celong Liu, Zhong Li, Ziyi Kou, Yi Xu, Chenliang Xu
2019 conf
VRCAI
Zhong Li, Lele Chen, Celong Liu, Yu Gao, Yuanzhou Ha, Chenliang Xu, Shuxue Quan, Yi Xu
2019 J jnl
Comput. Graph.
Ruikun Huang, Junli Zhao, Fuqing Duan, Xin Li, Celong Liu, Xiaodan Deng, Zhenkuan Pan, Zhongke Wu, Mingquan Zhou
2019 J jnl
Comput. Aided Geom. Des.
Xin Li, Kang Xie, Wenxing Hong, Celong Liu
2019 J jnl
J. Comput. Sci. Technol.
Masoud Zadghorban Lifkooee, Celong Liu, Yongqing Liang, Yimin Zhu, Xin Li
2018 J jnl
CoRR
Celong Liu, Xin Li
2017 J jnl
Comput. Aided Des.
Celong Liu, Wuyi Yu, Zhonggui Chen, Xin Li
2017 J jnl
Comput. Aided Des.
Xin Li, Wuyi Yu, Celong Liu
redb/extractors/js_extractors/js_suspicious_apis.py
← Index redb/extractors/js_extractors/js_suspicious_apis.py python
import inspect
from datetime import datetime, timezone
from typing import Any

from redb.extractors.enum import Tag
from redb.extractors.js_extractor import JSExtractor
from redb.extractors.js_extractors.js_patterns import CATEGORIES, PATTERNS


# Backwards-compatible export: `{category: [(raw_pattern_string, api_name), ...]}`
# in canonical PATTERNS insertion order (code_execution, network, filesystem,
# process, registry, crypto_encoding, dom_manipulation). Kept so external
# callers (notably JSDeobfuscationExtractor pre-cleanup) keep working until
# they are migrated to PATTERNS directly.
SUSPICIOUS_APIS: "dict[str, list[tuple[str, str]]]" = {}
for _name, _compiled in PATTERNS.items():
    SUSPICIOUS_APIS.setdefault(CATEGORIES[_name], []).append((_compiled.pattern, _name))


class JSSuspiciousAPIsExtractor(JSExtractor):

    def __init__(
        self, filepath, log, exporters=None, index_prefix=None,
        known_benign=False, known_malicious=False, source=None, context=None,
    ):
        super().__init__(
            filepath, log, exporters, index_prefix,
            known_benign, known_malicious, source, context=context,
        )
        self.api_findings = None
        self.log.debug(inspect.currentframe().f_code.co_name)

    def tag(self):
        return Tag.JS_SUSPICIOUS_APIS.value

    def _get_context_snippet(self, line, max_len=200):
        """Get a truncated context snippet around a match."""
        line = line.strip()
        if len(line) > max_len:
            return line[:max_len] + "..."
        return line

    def extract(self):
        src = self.js_source
        if not src:
            return None

        # Pass 1: shared per-sample scan over the raw source. The dict contains
        # entries for both PATTERNS and FEATURE_PATTERNS; the loop below only
        # consults PATTERNS keys, so feature-only entries are ignored.
        raw_scan = self._context.scan or {}
        raw_lines = self.lines

        # Pass 2: same patterns over the deobfuscated text, when the
        # deobfuscator produced something meaningfully different. APIs hidden
        # behind one obfuscation layer (Vjw0rm-style array.join + eval,
        # Dean-Edwards packers, jjencode, ...) only surface here. The scan is
        # cached on JSContext so JSDeobfuscationExtractor (which computes the
        # new_apis_found diff) reuses the same result.
        deobf_scan = self._context.scan_deobfuscated
        if deobf_scan:
            deobf_text, _ = self._context.deobfuscated
            deobf_lines = deobf_text.splitlines()
        else:
            deobf_lines = []

        findings = []
        # Iterate PATTERNS in canonical order so output is deterministic and
        # matches the historical category/pattern ordering. For each api_name,
        # raw findings take precedence; if an API is found only in the
        # deobfuscated text, we surface it as a row tagged revealed_by_deobf=1
        # with line numbers / snippets pulled from the deobfuscated source.
        for api_name in PATTERNS:
            raw_info = raw_scan.get(api_name)
            if raw_info:
                line_numbers = raw_info["lines"]
                lines_for_snippets = raw_lines
                revealed_by_deobf = 0
            else:
                deobf_info = deobf_scan.get(api_name)
                if not deobf_info:
                    continue
                line_numbers = deobf_info["lines"]
                lines_for_snippets = deobf_lines
                revealed_by_deobf = 1

            snippets = [
                self._get_context_snippet(lines_for_snippets[ln - 1])
                for ln in line_numbers[:3]
                if 0 < ln <= len(lines_for_snippets)
            ]
            findings.append({
                "api_name": api_name,
                "api_category": CATEGORIES[api_name],
                # Historical semantics: count = number of unique lines with a
                # match, not total in-source match count.
                "call_count": len(line_numbers),
                "line_numbers": line_numbers,
                "context_snippet": " | ".join(snippets),
                "revealed_by_deobf": revealed_by_deobf,
            })

        if not findings:
            return None

        self.api_findings = findings
        return findings

    def prepare_export_data(self, exporter_type: str) -> Any:
        if exporter_type == "ClickHouseExporter":
            if not self.api_findings:
                return None

            current_time = datetime.now(timezone.utc)
            data = []
            for f in self.api_findings:
                data.append([
                    self.sha256,
                    f['api_name'],
                    f['api_category'],
                    f['call_count'],
                    f['line_numbers'],
                    f['context_snippet'],
                    f['revealed_by_deobf'],
                    current_time,
                ])

            column_names = [
                "sha256", "api_name", "api_category",
                "call_count", "line_numbers", "context_snippet",
                "revealed_by_deobf",
                "analysis_date",
            ]

            column_type_names = [
                "FixedString(64)", "String", "LowCardinality(String)",
                "UInt32", "Array(UInt32)", "String",
                "UInt8",
                "DateTime64(3, 'UTC')",
            ]

            return (data, column_names, column_type_names)

    def get_clickhouse_table(self) -> str:
        return "redb_js_suspicious_apis"