Wei Jiang

12 papers Journal 10Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
Int. J. Digit. Earth
Longfei Wang, Tengfei Long, Wei Jiang, Elhadi Adam, Chunhui Wen, Weili Jiao, Guojin He
2025 J jnl
ISPRS Int. J. Geo Inf.
Wei Jiang, Xiaohui Ding, Fanping Kong, Gan Luo, Tengfei Long, Zhiguo Pang, Shiai Cui, Jie Liu, Elhadi Adam
2021 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Ranyu Yin, Guojin He, Wei Jiang, Yan Peng, Zhaoming Zhang, Moxuan Li, Chengjuan Gong
2020 J jnl
ISPRS Int. J. Geo Inf.
Hongxiang Guo, Guojin He, Wei Jiang, Ranyu Yin, Lei Yan, Wanchun Leng
2020 J jnl
Remote. Sens.
Shengrong Wei, Weili Jiao, Tengfei Long, Huichan Liu, Lu Bi, Wei Jiang, Boris A. Portnov, Ming Liu
2018 J jnl
ISPRS Int. J. Geo Inf.
Wei Jiang, Guojin He, Wanchun Leng, Tengfei Long, Guizhou Wang, Huichan Liu, Yan Peng, Ranyu Yin, Hongxiang Guo
2018 conf
BigSDM
Guojin He, Guizhou Wang, Tengfei Long, Huichan Liu, Weili Jiao, Wei Jiang, Ranyu Yin, Zhaoming Zhang, Wanchun Leng, Yan Peng, Xiaomei Zhang, Bo Cheng
2018 J jnl
Remote. Sens.
Wei Jiang, Guojin He, Tengfei Long, Yuan Ni, Huichan Liu, Yan Peng, Kenan Lv, Guizhou Wang
2018 J jnl
Sensors
Wei Jiang, Guojin He, Tengfei Long, Hongxiang Guo, Ranyu Yin, Wanchun Leng, Huichan Liu, Guizhou Wang
2017 J jnl
Remote. Sens.
Wei Jiang, Guojin He, Tengfei Long, Chen Wang, Yuan Ni, Ruiqi Ma
2017 J jnl
Remote. Sens.
Wei Jiang, Guojin He, Tengfei Long, Huichan Liu
2013 conf
GRMSE (1)
Yan Peng, Guojin He, Wei Jiang
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)