Wei Tang

80 papers A* 22A 3B 1C 1Journal 44Unranked 9
YearRankTypeTitle / Venue / Authors
2026 J jnl
IEEE Trans. Pattern Anal. Mach. Intell.
Liushuai Shi, Le Wang, Sanping Zhou, Wei Tang, Gang Hua
2025 J jnl
IEEE Trans. Pattern Anal. Mach. Intell.
Yonghao Dong, Le Wang, Sanping Zhou, Wei Tang, Gang Hua, Changyin Sun
2025 A* conf
CVPR
Sen Wang, Le Wang, Sanping Zhou, Jingyi Tian, Jiayi Li, Haowen Sun, Wei Tang
2025 J jnl
CoRR
Sen Wang, Le Wang, Sanping Zhou, Jingyi Tian, Jiayi Li, Haowen Sun, Wei Tang
2025 A* conf
CVPR
Xiaoqian Ruan, Pei Yu, Dian Jia, Hyeonjeong Park, Peixi Xiong, Wei Tang
2025 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Haoxuanye Ji, Le Wang, Sanping Zhou, Wei Tang, Nanning Zheng, Gang Hua
2025 A* conf
CVPR
Jingyi Tian, Le Wang, Sanping Zhou, Sen Wang, Jiayi Li, Haowen Sun, Wei Tang
2025 J jnl
CoRR
Zheng Qin, Le Wang, Yabing Wang, Sanping Zhou, Gang Hua, Wei Tang
2025 J jnl
CoRR
Sen Wang, Jingyi Tian, Le Wang, Zhimin Liao, Jiayi Li, Huaiyi Dong, Kun Xia, Sanping Zhou, Wei Tang, Gang Hua
2025 A* conf
CVPR
Haoyu Wang, Le Wang, Sanping Zhou, Jingyi Tian, Zheng Qin, Yabing Wang, Gang Hua, Wei Tang
2025 J jnl
Pattern Recognit.
Zhiming Zou, Dian Jia, Wei Tang
2024 J jnl
IEEE Trans. Multim.
Haoyue Shi, Le Wang, Sanping Zhou, Gang Hua, Wei Tang
2024 conf
ECCV (21)
Dian Jia, Xiaoqian Ruan, Kun Xia, Zhiming Zou, Le Wang, Wei Tang
2024 J jnl
CoRR
Kun Xia, Le Wang, Sanping Zhou, Gang Hua, Wei Tang
2024 J jnl
IEEE Trans. Image Process.
Haoxuanye Ji, Le Wang, Sanping Zhou, Wei Tang, Gang Hua
2024 conf
CVPR Workshops
Xiaoqian Ruan, Wei Tang
2024 conf
ECCV (6)
Haoyue Shi, Le Wang, Sanping Zhou, Gang Hua, Wei Tang
2024 A* conf
ACM Multimedia
Yabing Wang, Le Wang, Qiang Zhou, Zhibin Wang, Hao Li, Gang Hua, Wei Tang
2024 J jnl
CoRR
Yabing Wang, Le Wang, Qiang Zhou, Zhibin Wang, Hao Li, Gang Hua, Wei Tang
2024 A* conf
CVPR
Zheng Qin, Le Wang, Sanping Zhou, Panpan Fu, Gang Hua, Wei Tang
2024 J jnl
CoRR
Zheng Qin, Le Wang, Sanping Zhou, Panpan Fu, Gang Hua, Wei Tang
2024 J jnl
Pattern Recognit.
Haoxuanye Ji, Le Wang, Sanping Zhou, Wei Tang, Nanning Zheng, Gang Hua
2023 J jnl
IEEE Trans. Multim.
Wenlong Cheng, Wei Tang, Yan Huang, Yiwen Luo, Liang Wang
2023 J jnl
IEEE Trans. Pattern Anal. Mach. Intell.
Yuanhao Zhai, Le Wang, Wei Tang, Qilin Zhang, Nanning Zheng, David S. Doermann, Junsong Yuan, Gang Hua
2023 J jnl
IEEE Trans. Pattern Anal. Mach. Intell.
Zixin Zhu, Le Wang, Wei Tang, Nanning Zheng, Gang Hua
2023 J jnl
IEEE Trans. Multim.
Kun Xia, Le Wang, Yichao Shen, Sanping Zhou, Gang Hua, Wei Tang
2023 J jnl
IEEE Trans. Image Process.
Le Wang, Hongzhen Liu, Sanping Zhou, Wei Tang, Gang Hua
2023 A* conf
ICCV
Kun Xia, Le Wang, Sanping Zhou, Gang Hua, Wei Tang
2023 A* conf
CVPR
Zheng Qin, Sanping Zhou, Le Wang, Jinghai Duan, Gang Hua, Wei Tang
2023 J jnl
CoRR
Zheng Qin, Sanping Zhou, Le Wang, Jinghai Duan, Gang Hua, Wei Tang
2023 A* conf
AAAI
Yuxuan Wu, Le Wang, Sanping Zhou, Jinghai Duan, Gang Hua, Wei Tang
2023 J jnl
IEEE Trans. Pattern Anal. Mach. Intell.
Liushuai Shi, Le Wang, Chengjiang Long, Sanping Zhou, Wei Tang, Nanning Zheng, Gang Hua
2022 J jnl
IEEE Trans. Multim.
Yuanhao Zhai, Le Wang, Wei Tang, Qilin Zhang, Nanning Zheng, Gang Hua
2022 J jnl
IEEE Trans. Circuits Syst. Video Technol.
Chi Zhang, Zihang Lin, Liheng Xu, Zongliang Li, Wei Tang, Yuehu Liu, Gaofeng Meng, Le Wang, Li Li
2022 J jnl
Pattern Recognit.
Kun Xia, Le Wang, Sanping Zhou, Gang Hua, Wei Tang
2022 J jnl
Pattern Recognit.
Chengkang Shen, Peiyan Wang, Wei Tang
2022 A* conf
AAAI
Zixin Zhu, Le Wang, Wei Tang, Ziyi Liu, Nanning Zheng, Gang Hua
2022 A* conf
CVPR
Kun Xia, Le Wang, Sanping Zhou, Nanning Zheng, Wei Tang
2022 J jnl
CoRR
Kun Xia, Le Wang, Sanping Zhou, Nanning Zheng, Wei Tang
2022 J jnl
Pattern Recognit.
Haoyue Shi, Le Wang, Nanning Zheng, Gang Hua, Wei Tang
2022 J jnl
IEEE Trans. Pattern Anal. Mach. Intell.
Ziyi Liu, Le Wang, Qilin Zhang, Wei Tang, Nanning Zheng, Gang Hua
2021 A* conf
AAAI
Ziyi Liu, Le Wang, Qilin Zhang, Wei Tang, Junsong Yuan, Nanning Zheng, Gang Hua
2021 J jnl
CoRR
Ziyi Liu, Le Wang, Qilin Zhang, Wei Tang, Junsong Yuan, Nanning Zheng, Gang Hua
2021 B conf
FG
Zhiming Zou, Tianqi Liu, Dapeng Wu, Wei Tang
2021 A* conf
ICCV
Zixin Zhu, Wei Tang, Le Wang, Nanning Zheng, Gang Hua
2021 J jnl
CoRR
Zixin Zhu, Wei Tang, Le Wang, Nanning Zheng, Gang Hua
2021 A* conf
CVPR
Mingyuan Liu, Dan Schonfeld, Wei Tang
2021 J jnl
IEEE Trans. Image Process.
Le Wang, Rizhi Ding, Yuanhao Zhai, Qilin Zhang, Wei Tang, Nanning Zheng, Gang Hua
2021 J jnl
Neurocomputing
Le Wang, Changbo Zhai, Qilin Zhang, Wei Tang, Nanning Zheng, Gang Hua
2021 A* conf
ICCV
Haoxuanye Ji, Le Wang, Sanping Zhou, Wei Tang, Nanning Zheng, Gang Hua
2021 A* conf
ICCV
Zhiming Zou, Wei Tang
2021 J jnl
Int. J. Autom. Comput.
Wei Tang, Yan Huang, Liang Wang
2021 J jnl
CoRR
He Huang, Wei Tang, Jiawei Zhang, Philip S. Yu
2021 A* conf
ICCV
Fang Zheng, Le Wang, Sanping Zhou, Wei Tang, Zhenxing Niu, Nanning Zheng, Gang Hua
2021 J jnl
CoRR
Fang Zheng, Le Wang, Sanping Zhou, Wei Tang, Zhenxing Niu, Nanning Zheng, Gang Hua
2021 A* conf
AAAI
Ziyi Liu, Le Wang, Wei Tang, Junsong Yuan, Nanning Zheng, Gang Hua
2021 J jnl
CoRR
Ziyi Liu, Le Wang, Wei Tang, Junsong Yuan, Nanning Zheng, Gang Hua
2020 conf
ECCV (10)
Kenkun Liu, Rongqi Ding, Zhiming Zou, Le Wang, Wei Tang
2020 conf
ACCV (6)
He Huang, Shunta Saito, Yuta Kikuchi, Eiichi Matsumoto, Wei Tang, Philip S. Yu
2020 J jnl
CoRR
He Huang, Shunta Saito, Yuta Kikuchi, Eiichi Matsumoto, Wei Tang, Philip S. Yu
2020 A conf
BMVC
Zhiming Zou, Kenkun Liu, Le Wang, Wei Tang
2020 conf
ACCV (1)
Kenkun Liu, Zhiming Zou, Wei Tang
2020 A conf
BMVC
Haoyue Shi, Le Wang, Wei Tang, Nanning Zheng, Gang Hua
2020 A conf
BMVC
He Huang, Wei Tang, Philip S. Yu, Yuanwei Chen, Wenhao Zheng, Qing-Guo Chen
2020 J jnl
CoRR
He Huang, Yuanwei Chen, Wei Tang, Wenhao Zheng, Qing-Guo Chen, Yao Hu, Philip S. Yu
2020 conf
ECCV (6)
Yuanhao Zhai, Le Wang, Wei Tang, Qilin Zhang, Junsong Yuan, Gang Hua
2020 J jnl
CoRR
Yuanhao Zhai, Le Wang, Wei Tang, Qilin Zhang, Junsong Yuan, Gang Hua
2019 A* conf
CVPR
Wei Tang, Ying Wu
2018 J jnl
CoRR
Wei Tang, John Corring, Ying Wu, Gang Hua
2018 conf
ECCV (3)
Wei Tang, Pei Yu, Ying Wu
2017 A* conf
ICCV
Jiahuan Zhou, Pei Yu, Wei Tang, Ying Wu
2017 A* conf
AAAI
Wei Tang, Gang Hua, Liang Wang
2017 A* conf
ICCV
Wei Tang, Pei Yu, Jiahuan Zhou, Ying Wu
2016 C conf
IGARSS
Dan Wang, Zhenwei Shi, Wei Tang
2015 conf
CCCV (2)
Wei Tang, Yongzhen Huang, Liang Wang
2015 J jnl
IEEE Trans. Geosci. Remote. Sens.
Shuo Yang, Zhenwei Shi, Wei Tang
2015 J jnl
IEEE Trans. Geosci. Remote. Sens.
Wei Tang, Zhenwei Shi, Ying Wu, Changshui Zhang
2014 J jnl
IEEE Trans. Geosci. Remote. Sens.
Wei Tang, Zhenwei Shi, Ying Wu
2014 J jnl
IEEE Geosci. Remote. Sens. Lett.
Jiao Long, Zhenwei Shi, Wei Tang, Changshui Zhang
2014 J jnl
IEEE Trans. Geosci. Remote. Sens.
Zhenwei Shi, Wei Tang, Zhana Duren, Zhiguo Jiang
redb/extractors/js_extractors/js_context.py
← Index redb/extractors/js_extractors/js_context.py python
"""Per-sample shared state for the JavaScript extractor pipeline.

A `JSContext` is built exactly once per JS sample (in `workers.py`) and threaded
into every extractor that runs against that sample. It owns the disk read, the
decoded source text, the line-split cache, the Shannon text-entropy figure, the
shared `scan_source()` results, and the pyjsparser AST. Each of those is
computed lazily through `cached_property` so an extractor that doesn't need a
particular artefact does not pay for it.

Without this object, every JS extractor instance redoes the same disk read,
decode, scan, and (for any consumer) AST parse. With it, every extractor
shares one set of results.

`JSExtractor.__init__` accepts the context via a `context=` kwarg; if absent
(e.g. unit tests instantiating an extractor directly with `source=...`) it
builds a fresh context from the constructor arguments. Either path produces a
fully-populated context, so extractor code can always rely on
`self._context.scan` / `self._context.ast` / etc.
"""

from __future__ import annotations

import math
from collections import Counter
from dataclasses import dataclass
from functools import cached_property
from typing import Any, Dict, List, Optional

import chardet

from redb.extractors.js_extractors.js_patterns import scan_source


def decode_source(raw_bytes: bytes) -> str:
    """Decode raw JS bytes to text, honouring BOMs and falling back to chardet.

    Mirrors the historical `JSExtractor._decode_source` logic so existing tests
    continue to round-trip identically.
    """
    if not raw_bytes:
        return ""

    if raw_bytes[:3] == b"\xef\xbb\xbf":
        return raw_bytes[3:].decode("utf-8", errors="replace")
    if raw_bytes[:2] in (b"\xff\xfe", b"\xfe\xff"):
        return raw_bytes.decode("utf-16", errors="replace")

    try:
        return raw_bytes.decode("utf-8")
    except UnicodeDecodeError:
        pass

    try:
        detected = chardet.detect(raw_bytes)
        if detected and detected.get("encoding"):
            return raw_bytes.decode(detected["encoding"], errors="replace")
    except Exception:
        pass

    return raw_bytes.decode("latin-1", errors="replace")


def _text_entropy(text: str) -> float:
    """Shannon entropy of the character distribution of `text`, rounded to 4dp."""
    if not text:
        return 0.0
    counter = Counter(text)
    length = len(text)
    entropy = 0.0
    for count in counter.values():
        p = count / length
        if p > 0:
            entropy -= p * math.log2(p)
    return round(entropy, 4)


@dataclass
class JSContext:
    """Shared raw materials for one JS sample, consumed by every JS extractor.

    Cheap attributes (raw_bytes, source) are populated eagerly by the factory.
    Expensive ones (scan, ast) are cached_property — computed on first access
    and reused across every extractor that holds the same context.

    `content_type` is the magika label (e.g. `"javascript"`) carried alongside
    the source so the new code_text_content writer (and any future generic
    text-content writer) can record it without re-running magika. Defaults to
    `"javascript"` because by construction this context type is JS-specific;
    workers.py supplies the actual magika value when it builds the context.
    """

    filepath: str
    raw_bytes: bytes
    source: str
    log: Any = None
    content_type: str = "javascript"
    # Populated by JSStringsExtractor.extract() (the decoded/reconstructed
    # strings — hex/unicode/charcode/base64/concat unpacked into plaintext).
    # Read post-loop by the IOC plumbing in workers.py so any IOCs hidden
    # behind those encodings get scraped from the decoded form. Stays None
    # if JSStringsExtractor didn't run for this sample.
    decoded_strings: Optional[list] = None

    @cached_property
    def lines(self) -> List[str]:
        return self.source.splitlines() if self.source else []

    @cached_property
    def text_entropy(self) -> float:
        return _text_entropy(self.source)

    @cached_property
    def scan(self) -> Dict[str, Dict[str, object]]:
        """Result of running scan_source() exactly once over self.source."""
        return scan_source(self.source) if self.source else {}

    @cached_property
    def ast(self) -> Optional[Any]:
        """Lazy pyjsparser AST. Returns None if the parser is missing or fails.

        Extractors should treat None AST as "fall back to regex" — every
        AST-consuming extractor already handles that path.
        """
        if not self.source:
            return None
        try:
            import pyjsparser
            return pyjsparser.parse(self.source)
        except ImportError:
            if self.log is not None:
                self.log.debug("pyjsparser not installed, AST analysis skipped")
        except Exception as e:
            if self.log is not None:
                self.log.warning(f"AST parsing failed for {self.filepath}: {e}")
        return None

    @cached_property
    def deobfuscated(self) -> "tuple[Optional[str], Optional[str]]":
        """Run the configured JS deobfuscator (with jsbeautifier fallback) once
        per sample and cache the result. Returns `(text, normalizer_used)` or
        `(None, None)` if neither path produced output.

        Computed lazily on first access — samples whose pipeline never reads
        this don't pay the subprocess cost.
        """
        from redb.extractors.js_extractors.js_deobfuscator import deobfuscate
        return deobfuscate(self.source, self.log)

    @cached_property
    def scan_deobfuscated(self) -> Dict[str, Dict[str, object]]:
        """Result of running scan_source() exactly once over the deobfuscated
        text, keyed by PATTERNS only (FEATURE_PATTERNS are not consulted by
        the dual-pass consumers). Empty dict when there is no deobfuscated
        text or it equals the raw source.

        Two extractors consume the post-deobf API surface:
        `JSSuspiciousAPIsExtractor` (for revealed_by_deobf rows) and
        `JSDeobfuscationExtractor` (for the new_apis_found diff). Caching here
        means we scan the deobfuscated text once instead of twice per sample.
        """
        from redb.extractors.js_extractors.js_patterns import PATTERNS
        deobf_text, _ = self.deobfuscated
        if not deobf_text or deobf_text == self.source:
            return {}
        return scan_source(deobf_text, patterns=(PATTERNS,))

    @cached_property
    def xray(self):
        """Run @nodesecure/js-x-ray once per sample and cache the result.

        Returns an `XRayResult` (always — the function collapses every failure
        path to an empty result so callers don't have to special-case missing
        Node, missing package, timeouts, or parse errors). The
        `JSFeaturesExtractor` reads it for the obfuscator family name and for
        corroborating warning kinds; the heuristic falls back cleanly when
        `xray.obfuscator is None`.
        """
        from redb.extractors.js_extractors.js_xray import run
        return run(self.source, self.log)

    @classmethod
    def from_path(
        cls,
        filepath: str,
        log: Any = None,
        source: Optional[str] = None,
        raw_bytes: Optional[bytes] = None,
        content_type: str = "javascript",
    ) -> "JSContext":
        """Build a context from disk. `raw_bytes` and `source` are optional
        overrides — useful when the caller has already read or decoded the file.
        `content_type` is the magika label workers.py dispatched on; it lands
        on the context for the code_text_content writer to record.
        """
        if raw_bytes is None:
            with open(filepath, "rb") as f:
                raw_bytes = f.read()
        if source is None:
            source = decode_source(raw_bytes)
        return cls(
            filepath=filepath,
            raw_bytes=raw_bytes,
            source=source,
            log=log,
            content_type=content_type,
        )