Xiao Li

35 papers A* 10C 1Journal 23Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
CoRR
Xinlei Yin, Xiulian Peng, Xiao Li, Zhiwei Xiong, Yan Lu
2026 A* conf
CHI
Yi Zheng, Feier Qin, Xiao Li, Haibin Huang, Hanyao Wang, Xiaoyu Wang, Yan Lu, Yuan Zhang
2025 J jnl
CoRR
Xiao Li, Qi Chen, Xiulian Peng, Kai Yu, Xie Chen, Yan Lu
2025 J jnl
CoRR
Yang Li, Jinglu Wang, Lei Chu, Xiao Li, Shiu-Hong Kao, Ying-Cong Chen, Yan Lu
2025 J jnl
CoRR
Shiu-Hong Kao, Xiao Li, Jinglu Wang, Chi-Keung Tang, Yu-Wing Tai, Yan Lu
2023 A* conf
ICCV
Yushuang Wu, Xiao Li, Jinglu Wang, Xiaoguang Han, Shuguang Cui, Yan Lu
2023 J jnl
CoRR
Yushuang Wu, Xiao Li, Jinglu Wang, Xiaoguang Han, Shuguang Cui, Yan Lu
2023 A* conf
CVPR
Yue Gao, Yuan Zhou, Jinglu Wang, Xiao Li, Xiang Ming, Yan Lu
2023 J jnl
CoRR
Yue Gao, Yuan Zhou, Jinglu Wang, Xiao Li, Xiang Ming, Yan Lu
2023 A* conf
ICCV
Xiang Li, Jinglu Wang, Xiaohao Xu, Xiao Li, Bhiksha Raj, Yan Lu
2023 A* conf
CVPR
Yushuang Wu, Zizheng Yan, Ce Chen, Lai Wei, Xiao Li, Guanbin Li, Yihao Li, Shuguang Cui, Xiaoguang Han
2023 J jnl
CoRR
Yushuang Wu, Zizheng Yan, Ce Chen, Lai Wei, Xiao Li, Guanbin Li, Yihao Li, Shuguang Cui, Xiaoguang Han
2023 A* conf
CVPR
Mingfang Zhang, Jinglu Wang, Xiao Li, Yifei Huang, Yoichi Sato, Yan Lu
2023 J jnl
CoRR
Mingfang Zhang, Jinglu Wang, Xiao Li, Yifei Huang, Yoichi Sato, Yan Lu
2023 A* conf
CVPR
Kun Yan, Xiao Li, Fangyun Wei, Jinglu Wang, Chenbin Zhang, Ping Wang, Yan Lu
2023 J jnl
CoRR
Kun Yan, Xiao Li, Fangyun Wei, Jinglu Wang, Chenbin Zhang, Ping Wang, Yan Lu
2023 J jnl
IEEE Trans. Multim.
Xiang Li, Jinglu Wang, Xiao Li, Yan Lu
2022 J jnl
CoRR
Xiu Li, Xiao Li, Yan Lu
2022 A* conf
AAAI
Xiang Li, Jinglu Wang, Xiao Li, Yan Lu
2022 conf
ECCV (6)
Gusi Te, Xiu Li, Xiao Li, Jinglu Wang, Wei Hu, Yan Lu
2022 J jnl
CoRR
Gusi Te, Xiu Li, Xiao Li, Jinglu Wang, Wei Hu, Yan Lu
2022 J jnl
CoRR
Xiang Li, Jinglu Wang, Xiaohao Xu, Xiao Li, Yan Lu, Bhiksha Raj
2022 A* conf
AAAI
Xiaohao Xu, Jinglu Wang, Xiao Li, Yan Lu
2021 J jnl
ACM Trans. Multim. Comput. Commun. Appl.
Minxuan Lin, Fan Tang, Weiming Dong, Xiao Li, Changsheng Xu, Chongyang Ma
2021 J jnl
CoRR
Xiang Li, Jinglu Wang, Xiao Li, Yan Lu
2021 J jnl
CoRR
Xiaohao Xu, Jinglu Wang, Xiao Li, Yan Lu
2021 J jnl
CoRR
Xiang Li, Jinglu Wang, Xiao Li, Yan Lu
2020 J jnl
CoRR
Minxuan Lin, Fan Tang, Weiming Dong, Xiao Li, Chongyang Ma, Changsheng Xu
2019 C conf
CGI
Xiao Li, Peiran Ren, Yue Dong, Gang Hua, Xin Tong, Baining Guo
2019 J jnl
ACM Trans. Graph.
Duan Gao, Xiao Li, Yue Dong, Pieter Peers, Kun Xu, Xin Tong
2019 A* conf
CVPR
Xiao Li, Yue Dong, Pieter Peers, Xin Tong
2019 J jnl
CoRR
Xiao Li, Yue Dong, Pieter Peers, Xin Tong
2018 J jnl
CoRR
Xiao Li, Yue Dong, Pieter Peers, Xin Tong
2018 J jnl
Comput. Graph. Forum
Wenjie Ye, Xiao Li, Yue Dong, Pieter Peers, Xin Tong
2017 J jnl
ACM Trans. Graph.
Xiao Li, Yue Dong, Pieter Peers, Xin Tong
redb/extractors/decompiler/bninja/similarity/minhasher.py
← Index redb/extractors/decompiler/bninja/similarity/minhasher.py python
import logging
import random
from enum import Enum

from ..analysis.medium_level_normalization import MediumLevelNormalization

try:
    from .minhashcustom import MinHashCustom
    from ..analysis.low_level_normalization import LowLevelNormalization
except ImportError:
    # Fallback to absolute imports (for multiprocessing spawned processes)
    from redb.extractors.decompiler.bninja.similarity.minhashcustom import MinHashCustom
    from redb.extractors.decompiler.bninja.analysis.low_level_normalization import LowLevelNormalization

## Values for this configuration were extracted from https://github.com/danielplohmann/mcrit/blob/main/mcrit/config/MinHashConfig.py#L10
# Length in number of Shingles of which a minhash consists
# this value represents the length of sha256sum hash truncated
MINHASH_SIGNATURE_LENGTH: int = 64
# Number of bits per signature element (1-32 bits)
MINHASH_SIGNATURE_BITS: int = 8


class TokenKind(Enum):
    LLIL = "llil"
    TYPED_LLIL = "typed_llil"
    MLIL = "mlil"
    TYPED_MLIL = "typed_mlil"


class MinHasher:
    # stick to the default method
    MINHASH_STRATEGY_HASH_ALL = 1

    def __init__(self, seed, il_function, kind: TokenKind = TokenKind.LLIL):
        self._minhash_seeds = []
        self.il_func = il_function
        self.kind = kind
        self._minhash_permutation = []
        self._signature_segments = []
        self._initMinhashing(seed)

    def _initMinhashing(self, MINHASH_SEED=None):
        random.seed(MINHASH_SEED)
        # init sequence of seeds
        self._minhash_seeds = [
            random.randint(0, MinHashCustom.getHashMax()) for _ in range(MINHASH_SIGNATURE_LENGTH)
        ]

    def make_ngrams(self, tokens, n=3):
        """Take the ngrams of the IL we try to pass into the functions"""
        return [tuple(tokens[i:i+n]) for i in range(len(tokens) - n + 1)]

    def _extract_tokens(self):
        """Extract the IL tokens from the IL function, picking the right
        normalizer (LLIL/MLIL) and the right normalization mode
        (skeleton/typed) based on self.kind."""
        if self.kind in (TokenKind.LLIL, TokenKind.TYPED_LLIL):
            normalizer = LowLevelNormalization()
        elif self.kind in (TokenKind.MLIL, TokenKind.TYPED_MLIL):
            normalizer = MediumLevelNormalization()
        else:
            raise ValueError(f"Unsupported token kind: {self.kind}")

        # typed variants include operand type info, skeleton variants don't
        if self.kind in (TokenKind.TYPED_LLIL, TokenKind.TYPED_MLIL):
            normalize = normalizer.normalize_instr_with_operands
        else:
            normalize = normalizer.normalize_instruction_all_levels

        instructions = []
        for basic_block in self.il_func.basic_blocks:
            for il in basic_block:
                instructions.append(normalize(il))

        return instructions

    def calculateMinHash(self):
        """Calculate hash function every time, then take minimum shingle per shingler"""
        minhash_result = MinHashCustom(minhash_bits=MINHASH_SIGNATURE_BITS)
        minhash_signature = []

        tokens = self._extract_tokens()
        shingles = self.make_ngrams(tokens, n=3)

        # Functions with fewer than 3 IL instructions can't produce n-grams
        # Return empty minhash for such small functions (thunks, stubs, etc.)
        # Triggered by 39d8ad95b0323c37bd3134ab93ac4af44c66a1a8443a41c1ac02cec19bb2816a
        if not shingles:
            return []

        # Generate the MinHash
        for seed in self._minhash_seeds:
            hashed_shingles = [
                self.shingle_hash(shingle, seed) for shingle in shingles
            ]
            min_value = min(hashed_shingles)

            if MINHASH_SIGNATURE_BITS < 32:
                min_value %= (2 ** MINHASH_SIGNATURE_BITS)

            minhash_signature.append(min_value)

        minhash_result.setMinHash(minhash_signature)
        return minhash_result.getMinHashInt()

    def shingle_hash(self, shingle, hash_seed=0):
        """Produce a single 32bit UINT hash for a given shingle"""
        return MinHashCustom.hashData(shingle, hash_seed)