Weikang Huang

14 papers A* 1A 1B 3Journal 5Unranked 4
YearRankTypeTitle / Venue / Authors
2025 J jnl
CoRR
Lu Zhao, Rong Shi, Yue Sun, Shaoqing Zhang, Hongxing Niu, Yueqiang Chen, Baoguo He, Hongfeng Sun, Ziqing Yin, Shangchao Su, Zhiyan Cui, Liang Dong, Xiyuan Li, Lingbin Wang, Jianwei He, Jiesong Ma, Weikang Huang, Jianglei Tong, Dongdong Gao, Jian Zhang, Hong Tian, Zhaoqun Sun, Hui Shen
2025 J jnl
BMC Medical Imaging
Jiewen Chen, Fei Zhang, Shuitian Wu, Disi Liu, Liyang Yang, Meng Li, Ming Yin, Kun Ma, Ge Wen, Weikang Huang
2024 J jnl
Complex Intell. Syst.
Benting Wan, Jiao Zhang, Harish Garg, Weikang Huang
2024 J jnl
Neurocomputing
Wei Ma, Yao Li, Shiyong Lan, Wenwu Wang, Weikang Huang, Wujiang Zhu
2023 A conf
ICME
Wei Ma, Shiyong Lan, Weikang Huang, Wenwu Wang, Hongyu Yang, Yitong Ma, Yongjie Ma
2023 B conf
ICIP
Xiaoxiao Yin, Shiyong Lan, Weikang Huang, Yitong Ma, Wenwu Wang, Hongyu Yang, Yilin Zheng
2023 B conf
ICIP
Wei Ma, Shiyong Lan, Weikang Huang, Yitong Ma, Hongyu Yang, Wei Pan, Yilin Zheng
2022 conf
ICANN (2)
Caiyin Yang, Shiyong Lan, Weikang Huang, Wenwu Wang, Guoliang Liu, Hongyu Yang, Wei Ma, Piaoyang Li
2022 A* conf
ICML
Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, Pyang Li
2022 conf
ICANN (1)
Weikang Huang, Shiyong Lan, Wenwu Wang, Xuedong Yuan, Hongyu Yang, Piaoyang Li, Wei Ma
2021 B conf
ICIP
Guoliang Liu, Shiyong Lan, Ting Zhang, Weikang Huang, Wenwu Wang
2017 conf
ICARM
Zhiguo Lu, Weikang Huang, Xiaoguang Wang, Fangyong Yan, Ming Li, Zhuo Yan
2003 conf
Asian Test Symposium
Yingxiang Wang, Weikang Huang
1994 J jnl
J. Comput. Sci. Technol.
Weikang Huang, F. Lombard
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)